region merging techniques using information theory statistical measures the purpose of the current work is to propose under a statistical framework a family of unsupervised region merging techniques providing a set of the most relevant region based explanations of an image at different levels of analysis these techniques are characterized by general and nonparametric region models with neither color nor texture homogeneity assumptions and a set of innovative merging criteria based on information theory statistical measures the scale consistency of the partitions is assured through i a size regularization term into the merging criteria and a classical merging order or ii using a novel scale based merging order to avoid the region size homogeneity imposed by the use of a size regularization term moreover a partition significance index is defined to automatically determine the subset of most representative partitions from the created hierarchy most significant automatically extracted partitions show the ability to represent the semantic content of the image from a human point of view finally a complete and exhaustive evaluation of the proposed techniques is performed using not only different databases for the two main addressed problems object oriented segmentation of generic images and texture image segmentation but also specific evaluation features in each case under and oversegmentation error and a large set of region based pixel based and error consistency indicators respectively results are promising outperforming in most indicators both object oriented and texture state of the art segmentation techniques

graph theoretical clustering based on limited neighbourhood sets abstract a method for clustering data according to a visual model of clusters is proposed the method uses either of two graphs which are defined according to relative distance and based on the gabriel graph and the relative neighbourhood graph respectively the method is locally sensitive hierarchic and based on the concept of limited neighbourhood sets clusters that are either disjoint or homogeneous and separable by sharp changes in point density may be detected

multiregion image segmentation by parametric kernel graph cuts the purpose of this study is to investigate multiregion graph cut image partitioning via kernel mapping of the image data the image data is transformed implicitly by a kernel function so that the piecewise constant model of the graph cut formulation becomes applicable the objective function contains an original data term to evaluate the deviation of the transformed data within each segmentation region from the piecewise constant model and a smoothness boundary preserving regularization term the method affords an effective alternative to complex modeling of the original image data while taking advantage of the computational benefits of graph cuts using a common kernel function energy minimization typically consists of iterating image partitioning by graph cut iterations and evaluations of region parameters via fixed point computation a quantitative and comparative performance assessment is carried out over a large number of experiments using synthetic grey level data as well as natural images from the berkeley database the effectiveness of the method is also demonstrated through a set of experiments with real images of a variety of types such as medical synthetic aperture radar and motion maps

probabilistic joint image segmentation and labeling by figure ground composition we propose a layered statistical model for image segmentation and labeling obtained by combining independently extracted possibly overlapping sets of figure ground fg segmentations the process of constructing consistent image segmentations called tilings is cast as optimization over sets of maximal cliques sampled from a graph connecting all non overlapping figure ground segment hypotheses potential functions over cliques combine unary gestalt based figure qualities and pairwise compatibilities among spatially neighboring segments constrained by t junctions and the boundary interface statistics of real scenes building on the segmentation layer we further derive a joint image segmentation and labeling model jsl which given a bag of fgs constructs a joint probability distribution over both the compatible image interpretations tilings composed from those segments and over their labeling into categories the process of drawing samples from the joint distribution can be interpreted as first sampling tilings followed by sampling labelings conditioned on the choice of a particular tiling we learn the segmentation and labeling parameters jointly based on maximum likelihood with a novel estimation procedure we refer to as incremental saddle point approximation the partition function over tilings and labelings is increasingly more accurately approximated by including incorrect configurations that are rated as probable by candidate models during learning state of the art results are reported on the berkeley stanford and pascal voc datasets where an improvement of 28 was achieved for the segmentation task only tiling and an accuracy of 47 8 was obtained on the test set of voc12 for semantic labeling jsl

harmony potentials the hierarchical conditional random field hcrf model have been successfully applied to a number of image labeling problems including image segmentation however existing hcrf models of image segmentation do not allow multiple classes to be assigned to a single region which limits their ability to incorporate contextual information across multiple scales at higher scales in the image this representation yields an oversimplified model since multiple classes can be reasonably expected to appear within large regions this simplified model particularly limits the impact of information at higher scales since class label information at these scales is usually more reliable than at lower noisier scales neglecting this information is undesirable to address these issues we propose a new consistency potential for image labeling problems which we call the harmony potential it can encode any possible combination of labels penalizing only unlikely combinations of classes we also propose an effective sampling strategy over this expanded label set that renders tractable the underlying optimization problem our approach obtains state of the art results on two challenging standard benchmark datasets for semantic image segmentation pascal voc 2010 and msrc 21

affinity learning with diffusion on tensor product graph in many applications we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples often the pairwise similarities which are also called affinities are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples as observed in several recent approaches more reliable similarities can be obtained if the original similarities are diffused in the context of other data points where the context of each point is a set of points most similar to it compared to the existing methods our approach differs in two main aspects first instead of diffusing the similarity information on the original graph we propose to utilize the tensor product graph tpg obtained by the tensor product of the original graph with itself since tpg takes into account higher order information it is not a surprise that we obtain more reliable similarities however it comes at the price of higher order computational complexity and storage requirement the key contribution of the proposed approach is that the information propagation on tpg can be computed with the same computational complexity and the same amount of storage as the propagation on the original graph we prove that a graph diffusion process on tpg is equivalent to a novel iterative algorithm on the original graph which is guaranteed to converge after its convergence we obtain new edge weights that can be interpreted as new learned affinities we stress that the affinities are learned in an unsupervised setting we illustrate the benefits of the proposed approach for data manifolds composed of shapes images and image patches on two very different tasks of image retrieval and image segmentation with learned affinities we achieve the bull s eye retrieval score of 99 99 percent on the mpeg 7 shape dataset which is much higher than the state of the art algorithms when the data points are image patches the ncut with the learned affinities not only significantly outperforms the ncut with the original affinities but it also outperforms state of the art image segmentation methods

shape from sharp and motion blurred image pair motion blur due to camera shake is a common occurrence during image capture the apparent motion of a scene point in the image plane varies according to both camera motion and scene structure our objective is to infer the camera motion and the depth map of static scenes using motion blur as a cue to this end we use an unblurred blurred image pair initially we develop a technique to estimate the transformation spread function tsf which symbolizes the camera shake this technique uses blur kernels estimated at different points across the image based on the estimated tsf we recover the complete depth map of the scene within a regularization framework

scale and rotation invariant matching using linearly augmented trees we propose a novel linearly augmented tree method for efficient scale and rotation invariant object matching the proposed method enforces pairwise matching consistency defined on trees and high order constraints on all the sites of a template the pairwise constraints admit arbitrary metrics while the high order constraints use l1 norms and therefore can be linearized such a linearly augmented tree formulation introduces hyperedges and loops into the basic tree structure but different from a general loopy graph its special structure allows us to relax and decompose the optimization into a sequence of tree matching problems that are efficiently solvable by dynamic programming the proposed method also works on continuous scale and rotation parameters we can match with a scale up to any large value with the same efficiency our experiments on ground truth data and a variety of real images and videos show that the proposed method is efficient accurate and reliable

monotony of certain free group representations abstract let be a free nonabelian group and let be its boundary let h be one of the unitary representations of introduced earlier by the authors in 1996 duke math j 82 381 436 by its definition h acts on l 2 d h for a certain measure h this gives a boundary realization of h in a sense we make precise we show that h does not have any other boundary realizations and simultaneously provide a new proof that h is irreducible

using dynamic programming for solving variational problems in vision dynamic programming is discussed as an approach to solving variational problems in vision dynamic programming ensures global optimality of the solution is numerically stable and allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure as a specific example of the approach s efficacy applying dynamic programming to the energy minimizing active contours is described the optimization problem is set up as a discrete multistage decision process and is solved by a time delayed discrete dynamic programming algorithm a parallel procedure for decreasing computational costs is discussed

patchmatch filter edge aware filtering meets randomized search for visual correspondence though many tasks in computer vision can be formulated elegantly as pixel labeling problems a typical challenge discouraging such a discrete formulation is often due to computational efficiency recent studies on fast cost volume filtering based on efficient edge aware filters provide a fast alternative to solve discrete labeling problems with the complexity independent of the support window size however these methods still have to step through the entire cost volume exhaustively which makes the solution speed scale linearly with the label space size when the label space is huge or even infinite which is often the case for subpixel accurate stereo and optical flow estimation their computational complexity becomes quickly unacceptable developed to search approximate nearest neighbors rapidly the patchmatch method can significantly reduce the complexity dependency on the search space size but its pixel wise randomized search and fragmented data access within the 3d cost volume seriously hinder the application of efficient cost slice filtering this paper presents a generic and fast computational framework for general multi labeling problems called patchmatch filter pmf we explore effective and efficient strategies to weave together these two fundamental techniques developed in isolation i e patchmatch based randomized search and efficient edge aware image filtering by decompositing an image into compact superpixels we also propose superpixel based novel search strategies that generalize and improve the original patchmatch method further motivated to improve the regularization strength we propose a simple yet effective cross scale consistency constraint which handles labeling estimation for large low textured regions more reliably than a single scale pmf algorithm focusing on dense correspondence field estimation in this paper we demonstrate pmf s applications in stereo and optical flow our pmf methods achieve top tier correspondence accuracy but run much faster than other related competing methods often giving over 10 100 times speedup

normalized cuts and image segmentation we propose a novel approach for solving the perceptual grouping problem in vision rather than focusing on local features and their consistencies in the image data our approach aims at extracting the global impression of an image we treat image segmentation as a graph partitioning problem and propose a novel global criterion the normalized cut for segmenting the graph the normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups we show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion we applied this approach to segmenting static images as well as motion sequences and found the results to be very encouraging

interactive image segmentation via kernel propagation abstract in this paper we propose a new approach to interactive image segmentation via kernel propagation kp called kp cut the key to success in interactive image segmentation is to preserve characteristics of the user s interactive input and maintain data coherence effectively to achieve this we employ kp which is very effective in propagating the given supervised information into the entire data set kp first learns a small size seed kernel matrix and then propagates it into a large size full kernel matrix it is based on a learned kernel and thus can fit the given data better than a predefined kernel based on kp we first generate a small size seed kernel matrix from the user s interactive input then the seed kernel matrix is propagated into the full kernel matrix of the entire image during the propagation foreground objects are effectively segmented from background experimental results demonstrate that kp cut effectively extracts foreground objects from background and outperforms the state of the art methods for interactive image segmentation

hybrid image segmentation using watersheds and fast region merging a hybrid multidimensional image segmentation algorithm is proposed which combines edge and region based techniques through the morphological algorithm of watersheds an edge preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient then an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude this initial segmentation is the input to a computationally efficient hierarchical bottom up region merging process that produces the final segmentation the latter process uses the region adjacency graph rag representation of the image regions at each step the most similar pair of regions is determined minimum cost rag edge the regions are merged and the rag is updated traditionally the above is implemented by storing all rag edges in a priority queue we propose a significantly faster algorithm which additionally maintains the so called nearest neighbor graph due to which the priority queue size and processing time are drastically reduced the final segmentation provides due to the rag one pixel wide closed and accurately localized contours surfaces experimental results obtained with two dimensional three dimensional 2 d 3 d magnetic resonance images are presented

a multisize superpixel approach for salient object detection based on multivariate normal distribution estimation this paper presents a new method for salient object detection based on a sophisticated appearance comparison of multisize superpixels those superpixels are modeled by multivariate normal distributions in cie lab color space which are estimated from the pixels they comprise this fitting facilitates an efficient application of the wasserstein distance on the euclidean norm formula see text to measure perceptual similarity between elements saliency is computed in two ways on the one hand we compute global saliency by probabilistically grouping visually similar superpixels into clusters and rate their compactness on the other hand we use the same distance measure to determine local center surround contrasts between superpixels then an innovative locally constrained random walk technique that considers local similarity between elements balances the saliency ratings inside probable objects and background the results of our experiments show the robustness and efficiency of our approach against 11 recently published state of the art saliency detection methods on five widely used benchmark data sets

the complexity of class polynomial computation via floating point approximations we analyse the complexity of computing class polynomials that are an important ingredient for cm constructions of elliptic curves via complex floating point approximations of their roots the heart of the algorithm is the evaluation of modular functions in several arguments the fastest one of the presented approaches uses a technique devised by dupont to evaluate modular functions by newton iterations on an expression involving the arithmetic geometric mean it runs in time o d log 5 d log log d o d 1 epsilon o h 2 epsilon for any epsilon 0 where d is the cm discriminant and h is the degree of the class polynomial another fast algorithm uses multipoint evaluation techniques known from symbolic computation its asymptotic complexity is worse by a factor of log d up to logarithmic factors this running time matches the size of the constructed polynomials the estimate also relies on a new result concerning the complexity of enumerating the class group of an imaginary quadratic order and on a rigorously proven upper bound for the height of class polynomials

revisiting graph construction for fast image segmentation abstract in this paper we propose a simple but effective method for fast image segmentation we re examine the locality preserving character of spectral clustering by constructing a graph over image regions with both global and local connections our novel approach to build graph connections relies on two key observations 1 local region pairs that co occur frequently will have a high probability to reside on a common object 2 spatially distant regions in a common object often exhibit similar visual saliency which implies their neighborship in a manifold we present a novel energy function to efficiently conduct graph partitioning based on multiple high quality partitions we show that the generated eigenvector histogram based representation can automatically drive effective unary potentials for a hierarchical random field model to produce multi class segmentation sufficient experiments on the bsds500 benchmark large scale pascal voc and coco datasets demonstrate the competitive segmentation accuracy and significantly improved efficiency of our proposed method compared with other state of the arts

hierarchical image segmentation based on iterative contraction and merging in this paper we propose a new framework for hierarchical image segmentation based on iterative contraction and merging in the proposed framework we treat the hierarchical image segmentation problem as a sequel of optimization problems with each optimization process being realized by a contraction and merging process to identify and merge the most similar data pairs at the current resolution at the beginning we perform pixel based contraction and merging to quickly combine image pixels into initial region elements with visually indistinguishable intra region color difference after that we iteratively perform region based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation comparing with the state of the art techniques the proposed algorithm can not only produce high quality segmentation results in a more efficient way but also keep a lot of boundary details in the segmentation results

label information guided graph construction for semi supervised learning in the literature most existing graph based semi supervised learning methods only use the label information of observed samples in the label propagation stage while ignoring such valuable information when learning the graph in this paper we argue that it is beneficial to consider the label information in the graph learning stage specifically by enforcing the weight of edges between labeled samples of different classes to be zero we explicitly incorporate the label information into the state of the art graph learning methods such as the low rank representation lrr and propose a novel semi supervised graph learning method called semi supervised low rank representation this results in a convex optimization problem with linear constraints which can be solved by the linearized alternating direction method though we take lrr as an example our proposed method is in fact very general and can be applied to any self representation graph learning methods experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data and therefore is more effective for semi supervised learning tasks

feedback and surround modulated boundary detection edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes the human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations the classical approach assumes that these cells are only responsive to the stimulus present within their receptive fields however recent studies demonstrate that surrounding regions and inter areal feedback connections influence their responses significantly in this work we propose a biologically inspired edge detection model in which orientation selective neurons are represented through the first derivative of a gaussian function resembling double opponent cells in the primary visual cortex v1 in our model we account for four kinds of receptive field surround i e full far iso and orthogonal orientation whose contributions are contrast dependant the output signal from v1 is pooled in its perpendicular direction by larger v2 neurons employing a contrast variant centre surround kernel we further introduce a feedback connection from higher level visual areas to the lower ones the results of our model on three benchmark datasets show a big improvement compared to the current non learning and biologically inspired state of the art algorithms while being competitive to the learning based methods

complex hyperbolic geometry and hilbert spaces with complete pick kernels abstract suppose h is a finite dimensional reproducing kernel hilbert space of functions on x if h has the complete pick property then there is an isometric map from x with the metric induced by h into complex hyperbolic space ch n with its pseudohyperbolic metric we investigate the relationships between the geometry of x and the function theory of h and its multiplier algebra

local variation as a statistical hypothesis test the goal of image oversegmentation is to divide an image into several pieces each of which should ideally be part of an object one of the simplest and yet most effective oversegmentation algorithms is known as local variation lv felzenszwalb and huttenlocher in efficient graph based image segmentation ijcv 59 2 167 181 2004 in this work we study this algorithm and show that algorithms similar to lv can be devised by applying different statistical models and decisions thus providing further theoretical justification and a well founded explanation for the unexpected high performance of the lv approach some of these algorithms are based on statistics of natural images and on a hypothesis testing decision we denote these algorithms probabilistic local variation plv the best plv algorithm which relies on censored estimation presents state of the art results while keeping the same computational complexity of the lv algorithm

a region merging prior for variational level set image segmentation in current level set image segmentation methods the number of regions is assumed to known beforehand as a result it remains constant during the optimization of the objective functional how to allow it to vary is an important question which has been generally avoided this study investigates a region merging prior related to regions area to allow the number of regions to vary automatically during curve evolution thereby optimizing the objective functional implicitly with respect to the number of regions we give a statistical interpretation to the coefficient of this prior to balance its effect systematically against the other functional terms we demonstrate the validity and efficiency of the method by testing on real images of intensity color and motion

efficient graph based image segmentation this paper addresses the problem of segmenting an image into regions we define a predicate for measuring the evidence for a boundary between two regions using a graph based representation of the image we then develop an efficient segmentation algorithm based on this predicate and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties we apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph and illustrate the results with both real and synthetic images the algorithm runs in time nearly linear in the number of graph edges and is also fast in practice an important characteristic of the method is its ability to preserve detail in low variability image regions while ignoring detail in high variability regions

computing igusa class polynomials we give an algorithm that computes the genus two class polynomials of a primitive quartic cm eld k and we give a running time bound and a proof of correctness of this algorithm this is the rst proof of correctness and the rst running time bound of any algorithm that computes these polynomials our algorithm is based on the complex analytic method of spallek and van wamelen and runs in time e o 7 2 where is the discriminant of k

entropy rate clustering cluster analysis via maximizing a submodular function subject to a matroid constraint we propose a new objective function for clustering this objective function consists of two components the entropy rate of a random walk on a graph and a balancing term the entropy rate favors formation of compact and homogeneous clusters while the balancing function encourages clusters with similar sizes and penalizes larger clusters that aggressively group samples we present a novel graph construction for the graph associated with the data and show that this construction induces a matroid a combinatorial structure that generalizes the concept of linear independence in vector spaces the clustering result is given by the graph topology that maximizes the objective function under the matroid constraint by exploiting the submodular and monotonic properties of the objective function we develop an efficient greedy algorithm furthermore we prove an approximation bound of 1 2 for the optimality of the greedy solution we validate the proposed algorithm on various benchmarks and show its competitive performances with respect to popular clustering algorithms we further apply it for the task of superpixel segmentation experiments on the berkeley segmentation data set reveal its superior performances over the state of the art superpixel segmentation algorithms in all the standard evaluation metrics

simultaneous cast shadows illumination and geometry inference using hypergraphs the cast shadows in an image provide important information about illumination and geometry in this paper we utilize this information in a novel framework in order to jointly recover the illumination environment a set of geometry parameters and an estimate of the cast shadows in the scene given a single image and coarse initial 3d geometry we model the interaction of illumination and geometry in the scene and associate it with image evidence for cast shadows using a higher order markov random field mrf illumination model while we also introduce a method to obtain approximate image evidence for cast shadows capturing the interaction between light sources and geometry in the proposed graphical model necessitates higher order cliques and continuous valued variables which make inference challenging taking advantage of domain knowledge we provide a two stage minimization technique for the mrf energy of our model we evaluate our method in different datasets both synthetic and real our model is robust to rough knowledge of geometry and inaccurate initial shadow estimates allowing a generic coarse 3d model to represent a whole class of objects for the task of illumination estimation or the estimation of geometry parameters to refine our initial knowledge of scene geometry simultaneously with illumination estimation

recursive nearest agglomeration rena fast clustering for approximation of structured signals in this work we revisit fast dimension reduction approaches as with random projections and random sampling our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis such dimension reduction can be very efficient when the signals of interest have a strong structure such as with images we focus on this setting and investigate feature clustering schemes for data reductions that capture this structure an impediment to fast dimension reduction is then that good clustering comes with large algorithmic costs we address it by contributing a linear time agglomerative clustering scheme recursive nearest agglomeration rena unlike existing fast agglomerative schemes it avoids the creation of giant clusters we empirically validate that it approximates the data as well as traditional variance minimizing clustering schemes that have a quadratic complexity in addition we analyze signal approximation with feature clustering and show that it can remove noise improving subsequent analysis steps as a consequence data reduction by clustering features with rena yields very fast and accurate models enabling to process large datasets on budget our theoretical analysis is backed by extensive experiments on publicly available data that illustrate the computation efficiency and the denoising properties of the resulting dimension reduction scheme

simultaneous object classification and segmentation with high order multiple shape models shape models sms capturing the common features of a set of training shapes represent a new incoming object based on its projection onto the corresponding model given a set of learned sms representing different objects classes and an image with a new shape this work introduces a joint classification segmentation framework with a twofold goal first to automatically select the sm that best represents the object and second to accurately segment the image taking into account both the image information and the features and variations learned from the online selected model a new energy functional is introduced that simultaneously accomplishes both goals model selection is performed based on a shape similarity measure online determining which model to use at each iteration of the steepest descent minimization allowing for model switching and adaptation to the data high order sms are used in order to deal with very similar object classes and natural variability within them position and transformation invariance is included as part of the modeling as well the presentation of the framework is complemented with examples for the difficult task of simultaneously classifying and segmenting closely related shapes such as stages of human activities in images with severe occlusions

enhanced figure ground classification with background prior propagation we present an adaptive figure ground segmentation algorithm that is capable of extracting foreground objects in a generic environment starting from an interactively assigned background mask an initial background prior is defined and multiple soft label partitions are generated from different foreground priors by progressive patch merging these partitions are fused to produce a foreground probability map the probability map is then binarized via threshold sweeping to create multiple hard label candidates a set of segmentation hypotheses is formed using different evaluation scores from this set the hypothesis with maximal local stability is propagated as the new background prior and the segmentation process is repeated until convergence similarity voting is used to select a winner set and the corresponding hypotheses are fused to yield the final segmentation result experiments indicate that our method performs at or above the current state of the art on several data sets with particular success on challenging scenes that contain irregular or multiple connected foregrounds

an intensity texture model based level set method for image segmentation in this paper a novel level set segmentation model integrating the intensity and texture terms is proposed to segment complicated two phase nature images firstly an intensity term based on the global division algorithm is proposed which can better capture intensity information of image than the chan vese model cv particularly the cv model is a special case of the proposed intensity term under a certain condition secondly a texture term based on the adaptive scale local variation degree aslvd algorithm is proposed the aslvd algorithm adaptively incorporates the amplitude and frequency components of local intensity variation thus it can extract the non stationary texture feature accurately finally the intensity term and the texture term are jointly incorporated into level set and used to construct effective image segmentation model named as the intensity texture model since the intensity term and the texture term are complementary for image segmentation the intensity texture model has strong ability to accurately segment those complicated two phase nature images experimental results demonstrate the effectiveness of the proposed intensity texture model an intensity term based on the so called global division algorithm is proposed we extract the amplitude and frequency components of local intensity variation we propose the adaptive scale local variation degree algorithm as texture term the intensity and texture terms are integrated into level set energy functional

saliency region detection based on markov absorption probabilities in this paper we present a novel bottom up salient object detection approach by exploiting the relationship between the saliency detection and the markov absorption probability first we calculate a preliminary saliency map by the markov absorption probability on a weighted graph via partial image borders as background prior unlike most of the existing background prior based methods which treated all image boundaries as background we only use the left and top sides as background for simplicity the saliency of each element is defined as the sum of the corresponding absorption probability by several left and top virtual boundary nodes which are most similar to it second a better result is obtained by ranking the relevance of the image elements with foreground cues extracted from the preliminary saliency map which can effectively emphasize the objects against the background whose computation is processed similarly as that in the first stage and yet substantially different from the former one at last three optimization techniques content based diffusion mechanism superpixelwise depression function and guided filter are utilized to further modify the saliency map generalized at the second stage which is proved to be effective and complementary to each other both qualitative and quantitative evaluations on four publicly available benchmark data sets demonstrate the robustness and efficiency of the proposed method against 17 state of the art methods

image segmentation by map ml estimations image segmentation plays an important role in computer vision and image analysis in this paper image segmentation is formulated as a labeling problem under a probability maximization framework to estimate the label configuration an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori map estimation and the maximum likelihood ml estimation the map estimation problem is modeled with markov random fields mrfs and a graph cut algorithm is used to find the solution to the map estimation the ml estimation is achieved by computing the means of region features in a gaussian model our algorithm can automatically segment an image into regions with relevant textures or colors without the need to know the number of regions in advance its results match image edges very well and are consistent with human perception comparing to six state of the art algorithms extensive experiments have shown that our algorithm performs the best

the tractability of segmentation and scene analysis one of the fundamental problems in computer vision is the segmentation of an image into semantically meaningful regions based only on image characteristics a single segmentation can be determined using a linear number of evaluations of a uniformity predicate however minimising the number of regions is shown to be an np complete problem we also show that the variational approach to segmentation based on minimising a criterion combining the overall variance of regions and the number of regions also gives rise to an np complete problem when a library of object models is available segmenting the image becomes a problem of scene analysis a sufficient condition for the reconstruction of a 3d scene from a 2d image to be solvable in polynomial time is that the scene contains no cycles of mutually occluding objects and that no range information can be deduced from the image it is known that relaxing the no cycles condition renders the problem np complete we show that relaxing the no range information condition also produces an np complete problem

a tree structured framework for purifying complex clusters with structural roles of individual data how can we find a natural clustering of a complex dataset which may contain an unknown number of overlapping clusters of arbitrary shape and be contaminated by noise a tree structured framework is proposed in this paper to purify such clusters by exploring the structural role of each data in practice each individual object within the internal organization of the data has its own specific role centroid hub or outlier due to distinctive associations with their respective neighbors adjacent centroids always interact on each other and serve as mediate nodes of one tree being members of some cluster hubs closed to some centroid become leaf nodes responsible for the termination of the growth of trees outliers that weakly touch with any centroid are often discarded from any trees as global noise all the data can thus be labeled by a specified criterion of centroids connected structural consistency ccsc free of domain specific information our framework with ccsc could widely adapt to many clustering related applications theoretical and experimental contributions both confirm that our framework is easy to interpret and implement efficient and effective in complex clustering

fast approximate energy minimization via graph cuts many tasks in computer vision involve assigning a label such as disparity to every pixel a common constraint is that the labels should vary smoothly almost everywhere while preserving sharp discontinuities that may exist e g at object boundaries these tasks are naturally stated in terms of energy minimization the authors consider a wide class of energies with various smoothness constraints global minimization of these energy functions is np hard even in the simplest discontinuity preserving case therefore our focus is on efficient approximation algorithms we present two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves namely expansion moves and swap moves these moves can simultaneously change the labels of arbitrarily large sets of pixels in contrast many standard algorithms including simulated annealing use small moves where only one pixel changes its label at a time our expansion algorithm finds a labeling within a known factor of the global minimum while our swap algorithm handles more general energy functions both of these algorithms allow important cases of discontinuity preserving energies we experimentally demonstrate the effectiveness of our approach for image restoration stereo and motion on real data with ground truth we achieve 98 percent accuracy

directed connected operators asymmetric hierarchies for image filtering and segmentation connected operators provide well established solutions for digital image processing typically in conjunction with hierarchical schemes in graph based frameworks such operators basically rely on symmetric adjacency relations between pixels in this article we introduce a notion of directed connected operators for hierarchical image processing by also considering non symmetric adjacency relations the induced image representation models are no longer partition hierarchies i e trees but directed acyclic graphs that generalize standard morphological tree structures such as component trees binary partition trees or hierarchical watersheds we describe how to efficiently build and handle these richer data structures and we illustrate the versatility of the proposed framework in image filtering and image segmentation

distribution matching with the bhattacharyya similarity a bound optimization framework we present efficient graph cut algorithms for three problems 1 finding a region in an image so that the histogram or distribution of an image feature within the region most closely matches a given model 2 co segmentation of image pairs and 3 interactive image segmentation with a user provided bounding box each algorithm seeks the optimum of a global cost function based on the bhattacharyya measure a convenient alternative to other matching measures such as the kullback leibler divergence our functionals are not directly amenable to graph cut optimization as they contain non linear functions of fractional terms which make the ensuing optimization problems challenging we first derive a family of parametric bounds of the bhattacharyya measure by introducing an auxiliary labeling then we show that these bounds are auxiliary functions of the bhattacharyya measure a result which allows us to solve each problem efficiently via graph cuts we show that the proposed optimization procedures converge within very few graph cut iterations comprehensive and various experiments including quantitative and comparative evaluations over two databases demonstrate the advantages of the proposed algorithms over related works in regard to optimality computational load accuracy and flexibility

yet another cost aggregation over models in the recent decades we have witnessed the advent of local or non local filters for cost aggregation in stereo matching pushing the envelope of local methods to the degree of global methods while maintaining the efficiency a specific filter with a specific parameter setting may have a potential to best work for an image pair but may not guarantee equally good performance for other image pairs to address this problem we propose a mixture of experts model which applies a heterogeneous set of filters on the cost volume and adaptively combines the results we employ supervised learning to estimate per pixel mixing coefficients which are used to adaptively control the weight of the filter responses through experiments we show that the mixture model significantly reduces errors in disparity estimation and even outperforms the strategy of selecting the best per pixel filter from the pool of filters in the average sense

automatically finding clusters in normalized cuts normalized cuts is a state of the art spectral method for clustering by applying spectral techniques the data becomes easier to cluster and then k means is classically used unfortunately the number of clusters must be manually set and it is very sensitive to initialization moreover k means tends to split large clusters to merge small clusters and to favor convex shaped clusters in this work we present a new clustering method which is parameterless independent from the original data dimensionality and from the shape of the clusters it only takes into account inter point distances and it has no random steps the combination of the proposed method with normalized cuts proved successful in our experiments

regularized tree partitioning and its application to unsupervised image segmentation in this paper we propose regularized tree partitioning approaches we study normalized cut ncut and average cut acut criteria over a tree forming two approaches 1 normalized tree partitioning ntp and 2 average tree partitioning atp we give the properties that result in an efficient algorithm for ntp and atp in addition we present the relations between the solutions of ntp and atp over the maximum weight spanning tree of a graph and ncut and acut over this graph to demonstrate the effectiveness of the proposed approaches we show its application to image segmentation over the berkeley image segmentation data set and present qualitative and quantitative comparisons with state of the art methods

spherical functions and harmonic analysis on free groups let f r 1 be a free group with r generators in this paper we study a principal series and a complementary series of irreducible unitary representations of f which are defined through the action of f on its poisson boundary relative to a simple random walk we show that the regular representation of f can be written as a direct integral of the representations of the principal series and that the resulting harmonic analysis on the free group bears a close resemblance with the harmonic analysis of sl 2 ir

self validated labeling of markov random fields for image segmentation this paper addresses the problem of self validated labeling of markov random fields mrfs namely to optimize an mrf with unknown number of labels we present graduated graph cuts ggc a new technique that extends the binary s t graph cut for self validated labeling specifically we use the split and merge strategy to decompose the complex problem to a series of tractable subproblems in terms of gibbs energy minimization a suboptimal labeling is gradually obtained based upon a set of cluster level operations by using different optimization structures we propose three practical algorithms tree structured graph cuts tsgc net structured graph cuts nsgc and hierarchical graph cuts hgc in contrast to previous methods the proposed algorithms can automatically determine the number of labels properly balance the labeling accuracy spatial coherence and the labeling cost i e the number of labels and are computationally efficient independent to initialization and able to converge to good local minima of the objective energy function we apply the proposed algorithms to natural image segmentation experimental results show that our algorithms produce generally feasible segmentations for benchmark data sets and outperform alternative methods in terms of robustness to noise speed and preservation of soft boundaries

an optimal graph theoretic approach to data clustering theory and its application to image segmentation a novel graph theoretic approach for data clustering is presented and its application to the image segmentation problem is demonstrated the data to be clustered are represented by an undirected adjacency graph g with arc capacities assigned to reflect the similarity between the linked vertices clustering is achieved by removing arcs of g to form mutually exclusive subgraphs such that the largest inter subgraph maximum flow is minimized for graphs of moderate size approximately 2000 vertices the optimal solution is obtained through partitioning a flow and cut equivalent tree of g which can be efficiently constructed using the gomory hu algorithm 1961 however for larger graphs this approach is impractical new theorems for subgraph condensation are derived and are then used to develop a fast algorithm which hierarchically constructs and partitions a partially equivalent tree of much reduced size this algorithm results in an optimal solution equivalent to that obtained by partitioning the complete equivalent tree and is able to handle very large graphs with several hundred thousand vertices the new clustering algorithm is applied to the image segmentation problem the segmentation is achieved by effectively searching for closed contours of edge elements equivalent to minimum cuts in g which consist mostly of strong edges while rejecting contours containing isolated strong edges this method is able to accurately locate region boundaries and at the same time guarantees the formation of closed edge contours

image segmentation by a contrario simulation segmenting an image into homogeneous regions generally involves a decision criterion to establish whether two adjacent regions are similar decisions should be adaptive to get robust and accurate segmentation algorithms avoid hazardous a priori and have a clear interpretation we propose a decision process based on a contrario reasoning two regions are meaningfully different if the probability of observing such a difference in pure noise is very low since the existing analytical methods are intractable in our case we extend them to allow a mixed use of analytical computations and monte carlo simulations the resulting decision criterion is tested experimentally through a simple merging algorithm which can be used as a post filtering and validation step for existing segmentation methods

geometrically induced force interaction for three dimensional deformable models in this paper we propose a novel 3 d deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions this external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient the evolution of the deformable model is solved using the level set method so that topological changes are handled automatically the relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves the geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries and it gives the deformable model a high invariancy in initialization configurations the voxel interactions across the whole image domain provide a global view of the object boundary representation giving the external force a long attraction range the bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross boundary initializations and facilitates the handling of weak edges and broken boundaries in addition we show that by enhancing the geometrical interaction field with a nonlocal edge preserving algorithm the new deformable model can effectively overcome image noise we provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images and show that the proposed method achieves significant improvements against existing image gradient techniques

image segmentation with ratio cut this paper proposes a new cost function cut ratio for segmenting images using graph based methods the cut ratio is defined as the ratio of the corresponding sums of two different weights of edges along the cut boundary and models the mean affinity between the segments separated by the boundary per unit boundary length this new cost function allows the image perimeter to be segmented guarantees that the segments produced by bipartitioning are connected and does not introduce a size shape smoothness or boundary length bias the latter allows it to produce segmentations where boundaries are aligned with image edges furthermore the cut ratio cost function allows efficient iterated region based segmentation as well as pixel based segmentation these properties may be useful for some image segmentation applications while the problem of finding a minimum ratio cut in an arbitrary graph is np hard one can find a minimum ratio cut in the connected planar graphs that arise during image segmentation in polynomial time while the cut ratio alone is not sufficient as a baseline method for image segmentation it forms a good basis for an extended method of image segmentation when combined with a small number of standard techniques we present an implemented algorithm for finding a minimum ratio cut prove its correctness discuss its application to image segmentation and present the results of segmenting a number of medical and natural images using our techniques

normalized cut based saliency detection by adaptive multi level region merging existing salient object detection models favor over segmented regions upon which saliency is computed such local regions are less effective on representing object holistically and degrade emphasis of entire salient objects as a result the existing methods often fail to highlight an entire object in complex background toward better grouping of objects and background in this paper we consider graph cut more specifically the normalized graph cut ncut for saliency detection since the ncut partitions a graph in a normalized energy minimization fashion resulting eigenvectors of the ncut contain good cluster information that may group visual contents motivated by this we directly induce saliency maps via eigenvectors of the ncut contributing to accurate saliency estimation of visual clusters we implement the ncut on a graph derived from a moderate number of superpixels this graph captures both intrinsic color and edge information of image data starting from the superpixels an adaptive multi level region merging scheme is employed to seek such cluster information from ncut eigenvectors with developed saliency measures for each merged region encouraging performance is obtained after across level integration experiments by comparing with 13 existing methods on four benchmark datasets including msra 1000 sod sed and cssd show the proposed method ncut saliency results in uniform object enhancement and achieves comparable better performance to the state of the art methods

objective quality assessment of image retargeting by incorporating fidelity measures and inconsistency detection the tremendous growth in mobile devices has resulted in huge generation and usage of digital images image quality assessment is thus an important issue for mobile media applications in this paper we focus on the quality evaluation of images generated by content aware image retargeting in which the reference and the distorted images are of different sizes through retargeting many types of deformation inconsistency lead to shape distortion deformation artifacts and content information loss worsening its perceptual quality the deformation inconsistency occurs on different levels of the retargeted images limited by the accuracy of the alignment between the original and retargeted images previous methods only focus on pixel level and patch level fidelity analyses and fail to detect deformation inconsistency in this paper we improve the alignment algorithm and propose a three level representation of the retargeting process based on the analysis of this three level representation both fidelity measures and inconsistency detection are combined to determine the final retargeting quality the proposed algorithm is validated on the public data sets retargetme and cuhk experimental results demonstrate that inconsistency detection contributes to accurately assessing the image retargeting perceptual quality this inspires us to investigate more about deformation inconsistency to formulate the objective quality of image retargeting

automatic image segmentation by dynamic region merging this paper addresses the automatic image segmentation problem in a region merging style with an initially oversegmented image in which many regions or superpixels with homogeneous color are detected an image segmentation is performed by iteratively merging the regions according to a statistical test there are two essential issues in a region merging algorithm order of merging and the stopping criterion in the proposed algorithm these two issues are solved by a novel predicate which is defined by the sequential probability ratio test and the minimal cost criterion starting from an oversegmented image neighboring regions are progressively merged if there is an evidence for merging according to this predicate we show that the merging order follows the principle of dynamic programming this formulates the image segmentation as an inference problem where the final segmentation is established based on the observed image we also prove that the produced segmentation satisfies certain global properties in addition a faster algorithm is developed to accelerate the region merging process which maintains a nearest neighbor graph in each iteration experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm

globally optimal regions and boundaries as minimum ratio weight cycles we describe a new form of energy functional for the modeling and identification of regions in images the energy is defined on the space of boundaries in the image domain and can incorporate very general combinations of modeling information both from the boundary intensity gradients etc and from the interior of the region texture homogeneity etc we describe two polynomial time digraph algorithms for finding the global minima of this energy one of the algorithms is completely general minimizing the functional for any choice of modeling information it runs in a few seconds on a 256 spl times 256 image the other algorithm applies to a subclass of functionals but has the advantage of being extremely parallelizable neither algorithm requires initialization

multi region active contours with a single level set function segmenting an image into an arbitrary number of coherent regions is at the core of image understanding many formulations of the segmentation problem have been suggested over the past years these formulations include among others axiomatic functionals which are hard to implement and analyze and graph based alternatives which impose a non geometric metric on the problem we propose a novel method for segmenting an image into an arbitrary number of regions using an axiomatic variational approach the proposed method allows to incorporate various generic region appearance models while avoiding metrication errors in the suggested framework the segmentation is performed by level set evolution yet contrarily to most existing methods here multiple regions are represented by a single non negative level set function the level set function evolution is efficiently executed through the voronoi implicit interface method for multi phase interface evolution the proposed approach is shown to obtain accurate segmentation results for various natural 2d and 3d images comparable to state of the art image segmentation algorithms

image interpretation with a conceptual graph labeling over segmented images and detection of unexpected objects the labeling of the regions of a segmented image according to a semantic representation ontology is usually associated with the notion of understanding the high combinatorial aspect of this problem can be reduced with local checking of constraints between the elements of the ontology in the classical definition of finite domain constraint satisfaction problem it is assumed that the matching problem between regions and labels is bijective unfortunately in image interpretation the matching problem is often non univocal indeed images are often over segmented one object is made up of several regions this non univocal matching between data and a conceptual graph was not possible until a decisive step was accomplished by the introduction of arc consistency with bilevel constraint fdcsp b c however this extension is only adequate for a matching corresponding to surjective functions in medical image analysis the case of non functional relations is often encountered for example when an unexpected object like a tumor appears in this case the data cannot be mapped to the conceptual graph with a classical approach in this paper we propose an extension of the fdcsp b c to solve the constraint satisfaction problem for non functional relations

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

a reductive approach to hypergraph clustering an application to image segmentation in the last few years hypergraph based methods have gained considerable attention in the resolution of real world clustering problems since such a mode of representation can handle higher order relationships between elements compared to the standard graph theory the most popular and promising approach to hypergraph clustering arises from concepts in spectral hypergraph theory 53 and clustering is configured as a hypergraph cut problem where an appropriate objective function has to be optimized the spectral relaxation of this optimization problem allows to get a clustering that is close to the optimum but this approach generally suffers from its high computational demands especially in real world problems where the size of the data involved in their resolution becomes too large a natural way to overcome this limitation is to operate a reduction of the hypergraph where spectral clustering should be applied over a hypergraph of smaller size in this paper we introduce two novel hypergraph reduction algorithms that are able to maintain the hypergraph structure as accurate as possible these algorithms allowed us to design a new approach devoted to hypergraph clustering based on the multilevel paradigm that operates in three steps i hypergraph reduction ii initial spectral clustering of the reduced hypergraph and iii clustering refinement the accuracy of our hypergraph clustering framework has been demonstrated by extensive experiments with comparison to other hypergraph clustering algorithms and have been successfully applied to image segmentation for which an appropriate hypergraph based model have been designed the low running times displayed by our algorithm also demonstrates that the latter unlike the standard spectral clustering approach can handle datasets of considerable size

spatio temporal segmentation based on region merging this paper proposes a technique for spatio temporal segmentation to identify the objects present in the scene represented in a video sequence this technique processes two consecutive frames at a time a region merging approach is used to identify the objects in the scene starting from an oversegmentation of the current frame the objects are formed by iteratively merging regions together regions are merged based on their mutual spatio temporal similarity we propose a modified kolmogorov smirnov test for estimating the temporal similarity the region merging process is based on a weighted directed graph two complementary graph based clustering rules are proposed namely the strong rule and the weak rule these rules take advantage of the natural structures present in the graph experimental results on different types of scenes demonstrate the ability of the proposed technique to automatically partition the scene into its constituent objects

discovering discriminative graphlets for aerial image categories recognition recognizing aerial image categories is useful for scene annotation and surveillance local features have been demonstrated to be robust to image transformations including occlusions and clutters however the geometric property of an aerial image i e the topology and relative displacement of local features which is key to discriminating aerial image categories cannot be effectively represented by state of the art generic visual descriptors to solve this problem we propose a recognition model that mines graphlets from aerial images where graphlets are small connected subgraphs reflecting both the geometric property and color texture distribution of an aerial image more specifically each aerial image is decomposed into a set of basic components e g road and playground and a region adjacency graph rag is accordingly constructed to model their spatial interactions aerial image categories recognition can subsequently be casted as rag to rag matching based on graph theory rag to rag matching is conducted by comparing all their respective graphlets because the number of graphlets is huge we derive a manifold embedding algorithm to measure different sized graphlets after which we select graphlets that have highly discriminative and low redundancy topologies through quantizing the selected graphlets from each aerial image into a feature vector we use support vector machine to discriminate aerial image categories experimental results indicate that our method outperforms several state of the art object scene recognition models and the visualized graphlets indicate that the discriminative patterns are discovered by our proposed approach

regions adjacency graph applied to color image segmentation this paper presents different algorithms based on a combination of two structures of a graph and of two color image processing methods in order to segment color images the structures used in this study are the region adjacency graph and the associated line graph we see how these structures can enhance segmentation processes such as region growing or watershed transformation the principal advantage of these structures is that they give more weight to the adjacency relationships between regions than the usual methods we note nevertheless that this advantage leads the adjustment of more parameters than other methods to best refine the result of the segmentation we show that this adjustment is necessarily image dependent and observer dependent

graph cuts and efficient n d image segmentation combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics this paper focusses on possibly the simplest application of graph cuts segmentation of objects in image data despite its simplicity this application epitomizes the best features of combinatorial graph cuts methods in vision global optima practical efficiency numerical robustness ability to fuse a wide range of visual cues and constraints unrestricted topological properties of segments and applicability to n d problems graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes geodesic active contours and level sets the segmentation energies optimized by graph cuts combine boundary regularization with region based properties in the same fashion as mumford shah style functionals we present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s t graph cuts after the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in boykov and jolly 2001 this idea was widely studied in computer vision and graphics communities we provide links to a large number of known extensions based on iterative parameter re estimation and learning multi scale or hierarchical approaches narrow bands and other techniques for demanding photo video and medical applications

canny edge detection enhancement by scale multiplication the technique of scale multiplication is analyzed in the framework of canny edge detection a scale multiplication function is defined as the product of the responses of the detection filter at two scales edge maps are constructed as the local maxima by thresholding the scale multiplication results the detection and localization criteria of the scale multiplication are derived at a small loss in the detection criterion the localization criterion can be much improved by scale multiplication the product of the two criteria for scale multiplication is greater than that for a single scale which leads to better edge detection performance experimental results are presented

automatic estimation and removal of noise from a single image image denoising algorithms often assume an additive white gaussian noise awgn process that is independent of the actual rgb values such approaches cannot effectively remove color noise produced by today s ccd digital camera in this paper we propose a unified framework for two tasks automatic estimation and removal of color noise from a single image using piecewise smooth image models we introduce the noise level function nlf which is a continuous function describing the noise level as a function of image brightness we then estimate an upper bound of the real nlf by fitting a lower envelope to the standard deviations of per segment image variances for denoising the chrominance of color noise is significantly removed by projecting pixel values onto a line fit to the rgb values in each segment then a gaussian conditional random field gcrf is constructed to obtain the underlying clean image from the noisy input extensive experiments are conducted to test the proposed algorithm which is shown to outperform state of the art denoising algorithms

efficient graph based search for object detection in this paper we focus on the object localization problem in images given a single hand drawn example as the object model we propose a novel framework for shape based object detection and recognition which we formulate as a graph based search problem in our method we first propose five preprocessing procedures to reduce the irrelevant edge fragments in cluttered real images that often occur in edge detection then we build a graph to represent the edge images therefore our goal is to find the group of adjacent nodes in the graph that match well to the model contours finally we present a new evaluation method to verify the candidate hypotheses we did experiments on the ethz shape classes dataset and the inria horses dataset experimental results demonstrate that the proposed method achieves not only accurate object detection but also precise contour localization in cluttered real images comparisons with other recent template based matching methods further demonstrate the effectiveness and efficiency of the proposed method

rigid shape matching by segmentation averaging we use segmentations to match images by shape the new matching technique does not require point to point edge correspondence and is robust to small shape variations and spatial shifts to address the unreliability of segmentations computed bottom up we give a closed form approximation to an average over all segmentations our method has many extension yielding new algorithms for tracking object detection segmentation and edge preserving smoothing for segmentation instead of a maximum a posteriori approach we compute the central segmentation minimizing the average distance to all segmentations of an image for smoothing instead of smoothing images based on local structures we smooth based on the global optimal image structures our methods for segmentation smoothing and object detection perform competitively and we also show promising results in shape based tracking

nonlocal center surround reconstruction based bottom up saliency estimation many saliency models consider the feature extraction as the algorithmic core and the performance of their methods relies on the selection of the features to a great extent however there can hardly be a set of features effective to pop out the salient regions under various visual environments moreover because saliency is not tuned to certain visual features a location winning the spatial competition in any feature space can be defined as salient instead of seeking for or learning the features to highlight the difference between the salient areas and the background we focus more on the sparsity and uniqueness carried by the original image itself the source of all the features to propose a nonlocal reconstruction based saliency model in the proposed approach the saliency is measured by the sparse reconstruction residual of representing the central patch with a linear combination of its surrounding patches sampled in a nonlocal manner in addition this is generalized to model the global aspect saliency which provides a complement to the nonlocal saliency and improves the performance further as a generalization of itti et al s classical center surround comparison scheme the proposed approach performs well on images where itti et al s method fails as well as on general natural images numerical experiments show the proposed approach produces better results compared with the state of the art algorithms on three public databases highlightsa nonlocal reconstruction based bottom up saliency estimation method is proposed nonlocal image processing is employed to provide a better treatment of textures sparsity pursuit is very effective for removing duplicated similar image regions reconstruction residual is inherently coherent with the bottom up saliency our method is stable under three public datasets and consistent with human fixations

the scale and tidy subgroups for endomorphisms of totally disconnected locally compact groups the scale of an endomorphism alpha of a totally disconnected locally compact group g is the minimum index alpha u alpha u cap u for u a compact open subgroup of g a structural characterization of subgroups at which the minimum is attained is established this characterization extends the notion of subgroup tidy for alpha from the previously understood case when alpha is an automorphism to the case when alpha is merely an endomorphism

the segmentation of 3d images using the random walking technique on a randomly created image adjacency graph this paper considers the problem of image segmentation using the random walker algorithm in the case of 3d images the method uses an extreme amount of memory and time resources these are required in order to represent the corresponding enormous image graph and to solve the resulting sparse linear system having in mind these limitations this paper proposes techniques for the optimization of the random walker approach the optimization is obtained by processing supervoxels representing homogeneous image regions rather than single voxels a fast and efficient method for supervoxel determination is introduced a method for the creation of an image adjacency graph from an irregular grid of supervoxels is also proposed the results of applying the introduced approach to segmentation of 3d ct data sets are presented and compared with the results of the original random walker approach and other state of the art methods the accuracy and the computational overhead is regarded in the comparison the analysis of results shows that the modified method can be successfully applied for the segmentation of volumetric images and provides results in a reasonable time without a significant loss in the image segmentation accuracy it also outperforms the state of the art methods considered in the comparison

dense subgraph partition of positive hypergraphs in this paper we present a novel partition framework called dense subgraph partition dsp to automatically precisely and efficiently decompose a positive hypergraph into dense subgraphs a positive hypergraph is a graph or hypergraph whose edges except self loops have positive weights we first define the concepts of core subgraph conditional core subgraph and disjoint partition of a conditional core subgraph then define dsp based on them the result of dsp is an ordered list of dense subgraphs with decreasing densities which uncovers all underlying clusters as well as outliers a divide and conquer algorithm called min partition evolution is proposed to efficiently compute the partition dsp has many appealing properties first it is a nonparametric partition and it reveals all meaningful clusters in a bottom up way second it has an exact and efficient solution called min partition evolution algorithm the min partition evolution algorithm is a divide and conquer algorithm thus time efficient and memory friendly and suitable for parallel processing third it is a unified partition framework for a broad range of graphs and hypergraphs we also establish its relationship with the densest k subgraph problem d k s an np hard but fundamental problem in graph theory and prove that dsp gives precise solutions to d k s for all k in a graph dependent set called critical k set to our best knowledge this is a strong result which has not been reported before moreover as our experimental results show for sparse graphs especially web graphs the size of critical k set is close to the number of vertices in the graph we test the proposed partition framework on various tasks and the experimental results clearly illustrate its advantages

vcells simple and efficient superpixels using edge weighted centroidal voronoi tessellations vcells the proposed edge weighted centroidal voronoi tessellations ewcvts based algorithm is used to generate superpixels i e an oversegmentation of an image for a wide range of images the new algorithm is capable of generating roughly uniform subregions and nicely preserving local image boundaries the undersegmentation error is effectively limited in a controllable manner moreover vcells is very efficient with core computational cost at o k nc n in which k nc and n are the number of iterations superpixels and pixels respectively extensive qualitative discussions are provided together with the high quality segmentation results of vcells on a wide range of complex images the simplicity and efficiency of our model are demonstrated by complexity analysis time and accuracy evaluations

a bottom up approach for pancreas segmentation using cascaded superpixels and deep image patch labeling robust organ segmentation is a prerequisite for computer aided diagnosis quantitative imaging analysis pathology detection and surgical assistance for organs with high anatomical variability e g the pancreas previous segmentation approaches report low accuracies compared with well studied organs such as the liver or heart we present an automated bottom up approach for pancreas segmentation in abdominal computed tomography ct scans the method generates a hierarchical cascade of information propagation by classifying image patches at different resolutions and cascading segments superpixels the system contains four steps 1 decomposition of ct slice images into a set of disjoint boundary preserving superpixels 2 computation of pancreas class probability maps via dense patch labeling 3 superpixel classification by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks and 4 simple connectivity based post processing dense image patch labeling is conducted using two methods efficient random forest classification on image histogram location and texture features and more expensive but more accurate deep convolutional neural network classification on larger image windows i e with more spatial contexts over segmented 2 d ct slices by the simple linear iterative clustering approach are adopted through model parameter calibration and labeled at the superpixel level for positive pancreas or negative non pancreas or background classes the proposed method is evaluated on a data set of 80 manually segmented ct volumes using six fold cross validation its performance equals or surpasses other state of the art methods evaluated by leave one patient out with a dice coefficient of 70 7 and jaccard index of 57 9 in addition the computational efficiency has improved significantly requiring a mere 6 8 min per testing case versus 10 h for other methods the segmentation framework using deep patch labeling confidences is also more numerically stable as reflected in the smaller performance metric standard deviations finally we implement a multi atlas label fusion malf approach for pancreas segmentation using the same data set under six fold cross validation our bottom up segmentation method significantly outperforms its malf counterpart 70 7 13 0 versus 52 51 20 84 in dice coefficients

color constancy for multiple light sources color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes however in reality this assumption is often violated due to the presence of multiple light sources in this paper we will address more realistic scenarios where the uniform light source assumption is too restrictive first a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches rather than globally to the entire image after local patch based illuminant estimation these estimates are combined into more robust estimations and a local correction is applied based on a modified diagonal model quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene if the chromatic difference between these two illuminants is more than 1 the proposed framework outperforms algorithms based on the uniform light source assumption with error reduction up to approximately 30 otherwise when the chromatic difference is less than 1 and the scene can be considered to contain one approximately uniform light source the performance of the proposed method framework is similar to global color constancy methods

hierarchical image segmentation using correlation clustering in this paper we apply efficient implementations of integer linear programming to the problem of image segmentation the image is first grouped into superpixels and then local information is extracted for each pair of spatially adjacent superpixels given local scores on a map of several hundred superpixels we use correlation clustering to find the global segmentation that is most consistent with the local evidence we show that although correlation clustering is known to be np hard finding the exact global solution is still feasible by breaking the segmentation problem down into subproblems each such sub problem can be viewed as an automatically detected image part we can further accelerate the process by using the cutting plane method which provides a hierarchical structure of the segmentations the efficiency and improved performance of the proposed method is compared to several state of the art methods and demonstrated on several standard segmentation data sets