fast exact search in hamming space with multi index hashing there is growing interest in representing image data and feature descriptors using compact binary codes for fast near neighbor search although binary codes are motivated by their use as direct indices addresses into a hash table codes longer than 32 bits are not being used as such as it was thought to be ineffective we introduce a rigorous way to build multiple hash tables on binary code substrings that enables exact k nearest neighbor search in hamming space the approach is storage efficient and straight forward to implement theoretical analysis shows that the algorithm exhibits sub linear run time behavior for uniformly distributed codes empirical results show dramatic speedups over a linear scan baseline for datasets of up to one billion codes of 64 128 or 256 bits

aggregating local image descriptors into compact codes this paper addresses the problem of large scale image search three constraints have to be taken into account search accuracy efficiency and memory usage we first present and evaluate different ways of aggregating local image descriptors into a vector and show that the fisher kernel achieves better performance than the reference bag of visual words approach for any given vector dimension we then jointly optimize dimensionality reduction and indexing in order to obtain a precise vector comparison as well as a compact representation the evaluation shows that the image representation can be reduced to a few dozen bytes while preserving high accuracy searching a 100 million image data set takes about 250 ms on one processor core

coherency sensitive hashing coherency sensitive hashing csh extends locality sensitivity hashing lsh and patchmatch to quickly find matching patches between two images lsh relies on hashing which maps similar patches to the same bin in order to find matching patches patchmatch on the other hand relies on the observation that images are coherent to propagate good matches to their neighbors in the image plane using random patch assignment to seed the initial matching csh relies on hashing to seed the initial patch matching and on image coherence to propagate good matches in addition hashing lets it propagate information between patches with similar appearance i e map to the same bin this way information is propagated much faster because it can use similarity in appearance space or neighborhood in the image plane as a result csh is at least three to four times faster than patchmatch and more accurate especially in textured regions where reconstruction artifacts are most noticeable to the human eye we verified csh on a new large scale data set of 133 image pairs and experimented on several extensions including k nearest neighbor search the addition of rotation and matching three dimensional patches in videos

multiple bursts multiple looks and stream coherence in the release from informational masking in the simultaneous multitone masking paradigm introduced by neff and green percept psychophys 41 409 415 1987 the masker typically is a small number of tones having frequencies and levels that are randomly drawn on every presentation large amounts of masking for a pure tone signal often occur that are thought to reflect central rather than peripheral limitations on processing previous work from this laboratory has indicated that playing a rapid succession of randomly drawn multitone maskers in each observation interval dramatically reduces the amount of masking that is observed relative to a single burst sb in this multiple bursts different mbd procedure the signal tone is the only constant frequency component during the sequence of bursts and tends to perceptually segregate from the masker in this study the number of masker bursts and the interburst interval ibi were varied the goals were to determine how the release from masking relative to the sb condition depends on the number of bursts and to examine whether increasing the ibi would cause each burst to be processed independently if the latter were true it might disrupt the perception of signal stream coherence thereby diminishing the mbd advantage however multiple independent looks could also lead to an improvement in performance for those subjects showing large amounts of informational masking in the sb condition substantial reduction in masked thresholds occurred as the number of masker bursts increased while masking increased as ibi lengthened the results were not consistent with a simple version of a multiple look model in which the information from each burst was combined optimally but instead appear to be attributable to mechanisms involved in the perceptual organization of sounds

linear distance coding for image classification the feature coding pooling framework is shown to perform well in image classification tasks because it can generate discriminative and robust image representations the unavoidable information loss incurred by feature quantization in the coding process and the undesired dependence of pooling on the image spatial layout however may severely limit the classification in this paper we propose a linear distance coding ldc method to capture the discriminative information lost in traditional coding methods while simultaneously alleviating the dependence of pooling on the image spatial layout the core of the ldc lies in transforming local features of an image into more discriminative distance vectors where the robust image to class distance is employed these distance vectors are further encoded into sparse codes to capture the salient features of the image the ldc is theoretically and experimentally shown to be complementary to the traditional coding methods and thus their combination can achieve higher classification accuracy we demonstrate the effectiveness of ldc on six data sets two of each of three types specific object scene and general object i e flower 102 and pfid 61 scene 15 and indoor 67 caltech 101 and caltech 256 the results show that our method generally outperforms the traditional coding methods and achieves or is comparable to the state of the art performance on these data sets

a new algorithm for constructing approximate transformations for nonlinear systems an algorithm is provided for constructing an approximating transformation for a feedback linearizable system and for a system that is not feedback linearizable instead of approximating the given nonlinear system the authors approximate the transformation in a proper sense they assume that the lead variables of a transformation have a certain form with unknown parameters substitute these variables into the required equations and determine the unknown parameters through linear algebraic equations there is a tremendous savings in terms of number of equations and unknowns and it is sometimes possible to find exact or best possible transformations in a surprisingly small number of steps

edge sift discriminative binary descriptor for scalable partial duplicate mobile search as the basis of large scale partial duplicate visual search on mobile devices image local descriptor is expected to be discriminative efficient and compact our study shows that the popularly used histogram based descriptors such as scale invariant feature transform sift are not optimal for this task this is mainly because histogram representation is relatively expensive to compute on mobile platforms and loses significant spatial clues which are important for improving discriminative power and matching near duplicate image patches to address these issues we propose to extract a novel binary local descriptor named edge sift from the binary edge maps of scale and orientation normalized image patches by preserving both locations and orientations of edges and compressing the sparse binary edge maps with a boosting strategy the final edge sift shows strong discriminative power with compact representation furthermore we propose a fast similarity measurement and an indexing framework with flexible online verification hence the edge sift allows an accurate and efficient image search and is ideal for computation sensitive scenarios such as a mobile image search experiments on a large scale dataset manifest that the edge sift shows superior retrieval accuracy to oriented brief orb and is superior to sift in the aspects of retrieval precision efficiency compactness and transmission cost

a geometric isomorphism with applications to closed loop controls feedback equivalence of n state n 1 control systems satisfying certain regularity conditions divides such systems into two invariant classes we show that class one corresponds via a geometric isomorphism to classical lagrangian variational problems we prove the existence of time critical closed loop controls for systems that satisfy the nondegeneracy condition that the analogue of the hessian for the lagrangian problem have full rank we show that the vanishing of this hessian characterizes the control linear systems in class one and identify the rank condition for local controllability for such systems as the nonvanishing of a differential invariant the control linear systems in class two are also characterized by the vanishing of an invariant and the rank condition is identified

sources of variation in profile analysis ii component spacing dynamic changes and roving level profile analysis experiments have typically employed static profiles with constant frequency components spaced at equal intervals along a logarithmic frequency axis most periodic naturally occurring stimuli however have components that are harmonically related and vary dynamically in time one goal of these studies was to determine whether amplitude increment detection thresholds are different in dynamic harmonically spaced profiles compared to those for static log profiles and why such differences might exist a second goal was to determine the impact of roving levels within trial variation of level thresholds for static log profiles were on average 8 7 db lower than for static harmonic profiles a traditional filter bank model could not account for this result no consistent effect of dynamic contour an exponential rising frequency glide was observed thresholds were consistently poorer by 4 to 7 db when the level was roved but the differences in thresholds among the different profiles varied little it is proposed that the higher thresholds observed in static harmonic profiles may be accounted for by the more intense pitch strength associated with the harmonic profiles

spherical hashing binary code embedding with hyperspheres many binary code embedding schemes have been actively studied recently since they can provide efficient similarity search and compact data representations suitable for handling large scale image databases existing binary code embedding techniques encode high dimensional data by using hyperplane based hashing functions in this paper we propose a novel hypersphere based hashing function spherical hashing to map more spatially coherent data points into a binary code compared to hyperplane based hashing functions we also propose a new binary code distance function spherical hamming distance tailored for our hypersphere based binary coding scheme and design an efficient iterative optimization process to achieve both balanced partitioning for each hash function and independence between hashing functions furthermore we generalize spherical hashing to support various similarity measures defined by kernel functions our extensive experiments show that our spherical hashing technique significantly outperforms state of the art techniques based on hyperplanes across various benchmarks with sizes ranging from one to 75 million of gist bow and vlad descriptors the performance gains are consistent and large up to 100 percent improvements over the second best method among tested methods these results confirm the unique merits of using hyperspheres to encode proximity regions in high dimensional spaces finally our method is intuitive and easy to implement

spectral shape discrimination by hearing impaired and normal hearing listeners the ability to discriminate between sounds with different spectral shapes was evaluated for normal hearing and hearing impaired listeners listeners discriminated between a standard stimulus and a signal stimulus in which half of the standard components were decreased in level and half were increased in level in one condition the standard stimulus was the sum of six equal amplitude tones equal spl and in another the standard stimulus was the sum of six tones at equal sensation levels re audiometric thresholds for individual subjects equal sl spectral weights were estimated in conditions where the amplitudes of the individual tones were perturbed slightly on every presentation sensitivity was similar in all conditions for normal hearing and hearing impaired listeners the presence of perturbation and equal sl components increased thresholds for both groups but only small differences in weighting strategy were measured between the groups depending on whether the equal spl or equal sl condition was tested the average data suggest that normal hearing listeners may rely more on the central components of the spectrum whereas hearing impaired listeners may have been more likely to use the edges however individual weighting functions were quite variable especially for the hi listeners perhaps reflecting difficulty in processing changes in spectral shape due to hearing loss differences in weighting strategy without changes in sensitivity suggest that factors other than spectral weights such as internal noise or difficulty encoding a reference stimulus also may dominate performance

a survey on learning to hash nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest learning to hash is one of the major solutions to this problem and has been widely studied recently in this paper we present a comprehensive survey of the learning to hash algorithms categorize them according to the manners of preserving the similarities into pairwise similarity preserving multiwise similarity preserving implicit similarity preserving as well as quantization and discuss their relations we separate quantization from pairwise similarity preserving as the objective function is very different though quantization as we show can be derived from preserving the pairwise similarities in addition we present the evaluation protocols and the general performance analysis and point out that the quantization algorithms perform superiorly in terms of search accuracy search time cost and space cost finally we introduce a few emerging topics

trajectory based place recognition for efficient large scale localization place recognition is a core competency for any visual simultaneous localization and mapping system identifying previously visited places enables the creation of globally accurate maps robust relocalization and multi user mapping to match one place to another most state of the art approaches must decide a priori what constitutes a place often in terms of how many consecutive views should overlap or how many consecutive images should be considered together unfortunately such threshold dependencies limit their generality to different types of scenes in this paper we present a placeless place recognition algorithm using a novel match density estimation technique that avoids heuristically discretizing the space instead our approach considers place recognition as a problem of continuous matching between image streams automatically discovering regions of high match density that represent overlapping trajectory segments the algorithm uses well studied statistical tests to identify the relevant matching regions which are subsequently passed to an absolute pose algorithm to recover the geometric alignment we demonstrate the efficiency and accuracy of our methodology on three outdoor sequences including a comprehensive evaluation against ground truth from publicly available datasets that shows our approach outperforms several state of the art algorithms for place recognition furthermore we compare our overall algorithm to the currently best performing system for global localization and show how we outperform the approach on challenging indoor and outdoor datasets

local residual similarity for image re ranking abstract similarity measurement is an essential component in image retrieval systems while previous work is focused on generic distance estimation this paper investigates the problem of similarity estimation within a local neighborhood defined in the original feature space specifically our method is characterized in two aspects i e local and residual first of all we focus on a subset of the top ranked relevant images to a query with which anchors are discovered by methods such as averaging or clustering the anchors are then subtracted from the neighborhood features resulting in residual representations the proposed local residual similarity lrs homogenizes the feature distances within the local neighborhood effective and efficient image re ranking is achieved by calculating lrs between the query and the top ranked images the method constrains that relevant images should appear similar in both original and local residual feature space we evaluate the proposed method on two image retrieval benchmarks with global cnn representations demonstrating a consistent improvement on performance with very limited extra computational cost

hub discovery in partial correlation graphs one of the most important problems in large scale inference problems is the identification of variables that are highly dependent on several other variables when dependence is measured by partial correlations these variables identify those rows of the partial correlation matrix that have several entries with large magnitudes i e hubs in the associated partial correlation graph this paper develops theory and algorithms for discovering such hubs from a few observations of these variables we introduce a hub screening framework in which the user specifies both a minimum partial correlation rho and a minimum degree delta to screen the vertices the choice of rho and delta can be guided by our mathematical expressions for the phase transition correlation threshold rho c governing the average number of discoveries they can also be guided by our asymptotic expressions for familywise discovery rates under the assumption of large number p of variables fixed number n of multivariate samples and weak dependence under the null hypothesis that the dispersion covariance matrix is sparse these limiting expressions can be used to enforce familywise error constraints and to rank the discoveries in order of increasing statistical significance for n ll p the computational complexity of the proposed partial correlation screening method is low and is therefore highly scalable thus it can be applied to significantly larger problems than previous approaches the theory is applied to discovering hubs in a high dimensional gene microarray dataset

asymmetric distances for binary embeddings in large scale query by example retrieval embedding image signatures in a binary space offers two benefits data compression and search efficiency while most embedding algorithms binarize both query and database signatures it has been noted that this is not strictly a requirement indeed asymmetric schemes that binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy in this work we propose two general asymmetric distances that are applicable to a wide variety of embedding techniques including locality sensitive hashing lsh locality sensitive binary codes lsbc spectral hashing sh pca embedding pcae pcae with random rotations pcae rr and pcae with iterative quantization pcae itq we experiment on four public benchmarks containing up to 1m images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric hamming distance for all binary embedding techniques

feedback equivalence for general control systems abstract we discuss the solution to the problem of local equivalence of control systems with n states and p controls in a neighbourhood of a generic point under the lie pseudo group of local time independent feedback transformations we have shown earlier that this problem is identical with the problem of simple equivalence of the time optimal variational problem here we indicate the way in which this identification may be used to obtain closed loop time critical controls for general systems we show that the classification of general nonlinear systems depends on the classification of all n p dimensional affine subspaces of the space of symmetric forms in p variables and that the case of control linear systems depends on the classification of all n p dimensional affine subspaces of the space of skew forms in p variables we show that in the latter case the g structure is the prolongation of one determined by a state space transformation group we give a complete list of normal forms for control linear systems in the case p n 1

symmetry and the implementation of feedback linearization abstract we show that by making use of the symmetry group of the system one may find the linearizing feedback for any controllable linearizable system with p controls by solving independent completely integrable pfaffian systems whose dimensions are the multiplicities of the kronecker indices and carrying out purely algebraic calculations in particular if the kronecker indices are distinct this means solving p independent one dimensional completely integrable pfaffian systems

optimized product quantization product quantization pq is an effective vector quantization method a product quantizer can generate an exponentially large codebook at very low memory time cost the essence of pq is to decompose the high dimensional vector space into the cartesian product of subspaces and then quantize these subspaces separately the optimal space decomposition is important for the pq performance but still remains an unaddressed issue in this paper we optimize pq by minimizing quantization distortions w r t the space decomposition and the quantization codebooks we present two novel solutions to this challenging optimization problem the first solution iteratively solves two simpler sub problems the second solution is based on a gaussian assumption and provides theoretical analysis of the optimality we evaluate our optimized product quantizers in three applications i compact encoding for exhaustive ranking 1 ii building inverted multi indexing for non exhaustive search 2 and iii compacting image representations for image retrieval 3 in all applications our optimized product quantizers outperform existing solutions

global hashing system for fast image search hashing methods have been widely investigated for fast approximate nearest neighbor searching in large data sets most existing methods use binary vectors in lower dimensional spaces to represent data points that are usually real vectors of higher dimensionality we divide the hashing process into two steps data points are first embedded in a low dimensional space and the global positioning system method is subsequently introduced but modified for binary embedding we devise dataindependent and data dependent methods to distribute the satellites at appropriate locations our methods are based on finding the tradeoff between the information losses in these two steps experiments show that our data dependent method outperforms other methods in different sized data sets from 100k to 10m by incorporating the orthogonality of the code matrix both our data independent and data dependent methods are particularly impressive in experiments on longer bits

the ordinal relation preserving binary codes hashing algorithm has been widely used for efficient approximate nearest neighbor ann search learning optimal hashing functions has been given focus and it is still a challenge this paper aims to effectively and efficiently generate relative similarity preserving binary codes most existing hashing methods try to preserve the locality similarity by preserving direct distance similarity while ignoring the relative similarity which advantages in ann search in this paper this issue is solved by proposing the relative error which emphasizes that the ordinal relations in hamming space and euclidean space should be consistent with each other we learn hashing projection functions via two steps the first step adopts the lookup based mechanism to find the optimal binary codes of training data which can preserve the relative similarity and simultaneously adapt to data distribution the binary codes in the first step are considered as supervision information in the second step the objective of the second step is to learn hashing projection functions which can efficiently regenerate the binary codes in the first step aim to be in accordance with the property of data distribution the hyper internal tangent planes of two specified spheres are chosen as hashing projection functions assisted by these projection functions the time complexity of encoding process is greatly reduced experimental results on four public data sets demonstrate that our method outperforms many other state of the art methods author highlightsan effective and efficient binary coding framework which enjoys the merits of lookup based and hamming based methods is proposed relative error is proposed to preserve the relative sensitivity the effective tangent planes are adopted as hashing functions to avoid computing the complex classification problem the encoding error guarantees that the selected hashing functions have good performance

bilinear optimized product quantization for scalable visual content analysis product quantization pq has been recognized as a useful technique to encode visual feature vectors into compact codes to reduce both the storage and computation cost recent advances in retrieval and vision tasks indicate that high dimensional descriptors are critical to ensuring high accuracy on large scale data sets however optimizing pq codes with high dimensional data is extremely time consuming and memory consuming to solve this problem in this paper we present a novel pq method based on bilinear projection which can well exploit the natural data structure and reduce the computational complexity specifically we learn a global bilinear projection for pq where we provide both non parametric and parametric solutions the non parametric solution does not need any data distribution assumption the parametric solution can avoid the problem of local optima caused by random initialization and enjoys a theoretical error bound besides we further extend this approach by learning locally bilinear projections to fit underlying data distributions we show by extensive experiments that our proposed method dubbed bilinear optimization product quantization achieves competitive retrieval and classification accuracies while having significant lower time and space complexities

the gs algorithm for exact linearization to brunovsky normal form an algorithm utilizing the minimal number of integrations for the exact linearization of nonlinear systems to brunovsky normal form under nonlinear feedback is presented the tools which are involved are based on classical constructions appearing in the theory of exterior differential systems

spectral peak selection in spectral shape discrimination by normal hearing and hearing impaired listeners spectral shape discrimination thresholds were measured in the presence and absence of noise to determine whether normal hearing and hearing impaired listeners rely primarily on spectral peaks in the excitation pattern when discriminating between stimuli with different spectral shapes standard stimuli were the sum of 2 4 6 8 10 20 or 30 equal amplitude tones with frequencies fixed between 200 and 4000hz signal stimuli were generated by increasing and decreasing the levels of every other standard component the function relating the spectral shape discrimination threshold to the number of components n showed an initial decrease in threshold with increasing n and then an increase in threshold when the number of components reached 10 and 6 for normal hearing and hearing impaired listeners respectively the presence of a 50 db spl hz noise led to a 1 7db increase in threshold for normal hearing listeners and a 3 5db increase for hearing impaired listeners multichannel modeling and the relatively s

distributed adaptive binary quantization for fast nearest neighbor search hashing has been proved an attractive technique for fast nearest neighbor search over big data compared with the projection based hashing methods prototype based ones own stronger power to generate discriminative binary codes for the data with complex intrinsic structure however existing prototype based methods such as spherical hashing and k means hashing still suffer from the ineffective coding that utilizes the complete binary codes in a hypercube to address this problem we propose an adaptive binary quantization abq method that learns a discriminative hash function with prototypes associated with small unique binary codes our alternating optimization adaptively discovers the prototype set and the code set of a varying size in an efficient way which together robustly approximate the data relations our method can be naturally generalized to the product space for long hash codes and enjoys the fast training linear to the number of the training data we further devise a distributed framework for the large scale learning which can significantly speed up the training of abq in the distributed environment that has been widely deployed in many areas nowadays the extensive experiments on four large scale up to 80 million data sets demonstrate that our method significantly outperforms state of the art hashing methods with up to 58 84 performance gains relatively

3d shape matching via two layer coding view based 3d shape retrieval is a popular branch in 3d shape analysis owing to the high discriminative property of 2d views however many previous works do not scale up to large 3d shape databases we propose a two layer coding tlc framework to conduct shape matching much more efficiently the first layer coding is applied to pairs of views represented as depth images the spatial relationship of each view pair is captured with so called eigen angle which is the planar angle between the two views measured at the center of the 3d shape prior to the second layer coding the view pairs are divided into subsets according to their eigen angles consequently view pairs that differ significantly in their eigen angles are encoded with different codewords which implies that spatial arrangement of views is preserved in the second layer coding the final feature vector of a 3d shape is the concatenation of all the encoded features from different subsets which is used for efficient indexing directly tlc is not limited to encode the local features from 2d views but can be also applied to encoding 3d features exhaustive experimental results confirm that tlc achieves state of the art performance in both retrieval accuracy and efficiency

differential flatness and absolute equivalence of nonlinear control systems this paper presents a formulation of differential flatness a concept originally introduced by fliess levine martin and rouchon in terms of absolute equivalence between exterior differential systems systems that are differentially flat have several useful properties that can be exploited to generate effective control strategies for nonlinear systems the original definition of flatness was given in the context of differential algebra and required that all mappings be meromorphic functions the formulation of flatness presented here does not require any algebraic structure and allows one to use tools from exterior differential systems to help characterize differentially flat systems in particular it is shown that under regularity assumptions and in the case of single input control systems i e codimension 2 pfaffian systems a system is differentially flat if and only if it is feedback linearizable via static state feedback in higher codimensions our approach does not allow one to prove that feedback linearizability about an equilibrium point and flatness are equivalent one must be careful with the role of time as well as the use of prolongations that may not be realizable as dynamic feedback in a control setting applications of differential flatness to nonlinear control systems and open questions are also discussed

robust quantization for general similarity search the recent years have witnessed the emerging of vector quantization vq techniques for efficient similarity search vq partitions the feature space into a set of codewords and encodes data points as integer indices using the codewords then the distance between data points can be efficiently approximated by simple memory lookup operations by the compact quantization the storage cost and searching complexity are significantly reduced thereby facilitating efficient large scale similarity search however the performance of several celebrated vq approaches degrades significantly when dealing with noisy data in addition it can barely facilitate a wide range of applications as the distortion measurement only limits to ell 2 norm to address the shortcomings of the squared euclidean ell 2 2 norm loss function employed by the vq approaches in this paper we propose a novel robust and general vq framework named rgvq to enhance both robustness and generalization of vq approaches specifically a ell p q norm loss function is proposed to conduct the ell p norm similarity search rather than the ell 2 norm search and the q th order loss is used to enhance the robustness despite the fact that changing the loss function to ell p q norm makes vq approaches more robust and generic it brings us a challenge that a non smooth and non convex orthogonality constrained ell p q norm function has to be minimized to solve this problem we propose a novel and efficient optimization scheme and specify it to vq approaches and theoretically prove its convergence extensive experiments on benchmark data sets demonstrate that the proposed rgvq is better than the original vq for several approaches especially when searching similarity in noisy data

scalable similarity search with topology preserving hashing hashing based similarity search techniques is becoming increasingly popular in large data sets to capture meaningful neighbors the topology of a data set which represents the neighborhood relationships between its subregions and the relative proximities between the neighbors of each subregion e g the relative neighborhood ranking of each subregion should be exploited however most existing hashing methods are developed to preserve neighborhood relationships while ignoring the relative neighborhood proximities moreover most hashing methods lack in providing a good result ranking since there are often lots of results sharing the same hamming distance to a query in this paper we propose a novel hashing method to solve these two issues jointly the proposed method is referred to as topology preserving hashing tph tph is distinct from prior works by also preserving the neighborhood ranking based on this framework we present three different tph methods including linear unsupervised tph semisupervised tph and kernelized tph particularly our unsupervised tph is capable of mining semantic relationship between unlabeled data without supervised information extensive experiments on four large data sets demonstrate the superior performances of the proposed methods over several state of the art unsupervised and semisupervised hashing techniques

isosceles constraints for person re identification in the existing works of person re identification reid batch hard triplet loss has achieved great success however it only cares about the hardest samples within the batch for any probe there are massive mismatched samples crucial samples outside the batch which are closer than the matched samples to reduce the disruptive influence of crucial samples we propose a novel isosceles contraint for triplet theoretically we show that if a matched pair has equal distance to any one of mismatched sample the matched pair should be infinitely close motivated by this the isosceles constraint is designed for the two mismatched pairs of each triplet to restrict some matched pairs with equal distance to different mismatched samples meanwhile to ensure that the distance of mismatched pairs are larger than the matched pairs margin constraints are necessary minimizing the isosceles and margin constraints with respect to the feature extraction network makes the matched pairs closer and the mismatched pairs farther away than the matched ones by this way crucial samples are effectively reduced and the performance on reid is improved greatly likewise our isosceles contraint can be applied to quadruplet as well comprehensive experimental evaluations on market 1501 dukemtmc reid and cuhk03 datasets demonstrate the advantages of our isosceles constraint over the related state of the art approaches

feedback equivalence of control systems abstract cartan s method of equivalence is applied to the problem of equivalence of 2 state systems with scalar control under feedback the differential invariants produced by the method completely characterize equivalence classes the phenomenon of linearizability is associated with the presence of infinite lie pseudogroups the generic non linearizable case has a geometrically defined variational problem which yields a time optimal closed loop control

nonlinearizable single input control systems do not admit stationary symmetries the main result of the paper states that almost any analytic single input control system which is truly nonlinear that is not feedback linearizable with controllable linearization at an equilibrium point does not admit any symmetry preserving that point by almost any system we mean that we exclude a small class of odd systems that admit just one nontrivial symmetry conjugated to minus identity the obtained results are based on a recent classification of nonlinear single input systems under formal feedback we also describe symmetries of feedback linearizable systems

osri a rotationally invariant binary descriptor binary descriptors are becoming widely used in computer vision field because of their high matching efficiency and low memory requirements since conventional approaches which first compute a floating point descriptor then binarize it are computationally expensive some recent efforts have focused on directly computing binary descriptors from local image patches although these binary descriptors enable a significant speedup in processing time their performances usually drop a lot due to orientation estimation errors and limited description abilities to address these issues we propose a novel binary descriptor based on the ordinal and spatial information of regional invariants osris over a rotation invariant sampling pattern our main contributions are twofold 1 each bit in osri is computed based on difference tests of regional invariants over pairwise sampling regions instead of difference tests of pixel intensities commonly used in existing binary descriptors which can significantly enhance the discriminative ability and 2 rotation and illumination changes are handled well by ordering pixels according to their intensities and gradient orientations meanwhile which is also more reliable than those methods that resort to a reference orientation for rotation invariance besides a statistical analysis of discriminative abilities of different parts in the descriptor is conducted to design a cascade filter which can reject nonmatching descriptors at early stages by comparing just a small portion of the whole descriptor further reducing the matching time extensive experiments on four challenging data sets oxford 53 objects zubud and kentucky show that osri significantly outperforms two state of the art binary descriptors freak and orb the matching performance of osri with only 512 bits is also better than the well known floating point descriptor sift 4k bits and is comparable with the state of the art floating point descriptor mrogh 6k bits while it is two orders of magnitude faster to match than sift and mrogh

effect of temporal position proportional variance and proportional duration on decision weights in temporal pattern discrimination two experiments investigated how listeners allocate their attention to different segments of a temporal pattern the experiments allowed a direct test of the predictions of the proportion of total duration ptd rule and the component relative entropy core model listeners had to decide whether two sequences of nine tones had the same or different temporal patterns tone duration 25 ms tone frequency 1000 hz a sequence s temporal pattern was determined by the time intervals between each tone s offset and the next tone s onset on same trials the time intervals at corresponding temporal positions in the two sequences were identical on different trials the corresponding time intervals were randomly varied listener attention to different temporal positions within a sequence was assessed by calculating the decision weights at each position the results supported the core model and were inconsistent with the ptd rule manipulating the mean of the time intervals within the sequence had no consistent ef

interferences in match kernels we consider the design of an image representation that embeds and aggregates a set of local descriptors into a single vector popular representations of this kind include the bag of visual words the fisher vector and the vlad when two such image representations are compared with the dot product the image to image similarity can be interpreted as a match kernel in match kernels one has to deal with interference i e with the fact that even if two descriptors are unrelated their matching score may contribute to the overall similarity we formalise this problem and propose two related solutions both aimed at equalising the individual contributions of the local descriptors in the final representation these methods modify the aggregation stage by including a set of per descriptor weights they differ by the objective function that is optimised to compute those weights the first is a democratisation strategy that aims at equalising the relative importance of each descriptor in the set comparison metric the second one involves equalising the match of a single descriptor to the aggregated vector these concurrent methods give a substantial performance boost over the state of the art in image search with short or mid size vectors as demonstrated by our experiments on standard public image retrieval benchmarks

robust imagegraph rank level feature fusion for image search recently feature fusion has demonstrated its effectiveness in image search however bad features and inappropriate parameters usually bring about false positive images i e outliers leading to inferior performance therefore a major challenge of fusion scheme is how to be robust to outliers towards this goal this paper proposes a rank level framework for robust feature fusion first we define rank distance to measure the relevance of images at rank level based on it bayes similarity is introduced to evaluate the retrieval quality of individual features through which true matches tend to obtain higher weight than outliers then we construct the directed imagegraph to encode the relationship of images each image is connected to its k nearest neighbors with an edge and the edge is weighted by bayes similarity multiple rank lists resulted from different methods are merged via imagegraph furthermore on the fused imagegraph local ranking is performed to re order the initial rank lists it aims at local optimization and thus is more robust to global outliers extensive experiments on four benchmark data sets validate the effectiveness of our method besides the proposed method outperforms two popular fusion schemes and the results are competitive to the state of the art

locality sensitive hashing for chi2 distance in the past 10 years new powerful algorithms based on efficient data structures have been proposed to solve the problem of nearest neighbors search or approximate nearest neighbors search if the euclidean locality sensitive hashing algorithm which provides approximate nearest neighbors in a euclidean space with sublinear complexity is probably the most popular the euclidean metric does not always provide as accurate and as relevant results when considering similarity measure as the earth mover distance and 2 distances in this paper we present a new lsh scheme adapted to 2 distance for approximate nearest neighbors search in high dimensional spaces we define the specific hashing functions we prove their local sensitivity and compare through experiments our method with the euclidean locality sensitive hashing algorithm in the context of image retrieval on real image databases the results prove the relevance of such a new lsh scheme either providing far better accuracy in the context of image retrieval than euclidean scheme for an equivalent speed or providing an equivalent accuracy but with a high gain in terms of processing speed

uncorrelated component analysis based hashing the approximate nearest neighbor ann search problem is important in applications such as information retrieval several hashing based search methods that provide effective solutions to the ann search problem have been proposed however most of these focus on similarity preservation and coding error minimization and pay little attention to optimizing the precision recall curve or receiver operating characteristic curve in this paper we propose a novel projection based hashing method that attempts to maximize precision and recall we first introduce an uncorrelated component analysis uca transformation by examining precision and recall and then propose a uca based hashing method the proposed method is evaluated with a variety of data sets the results show that uca based hashing outperforms state of the art methods and has computationally efficient training and encoding processes

image classification with the fisher vector theory and practice a standard approach to describe an image for classification and retrieval purposes is to extract a set of local patch descriptors encode them into a high dimensional vector and pool them into an image level signature the most common patch encoding strategy consists in quantizing the local descriptors into a finite set of prototypical elements this leads to the popular bag of visual words representation in this work we propose to use the fisher kernel framework as an alternative patch encoding strategy we describe patches by their deviation from an universal generative gaussian mixture model this representation which we call fisher vector has many advantages it is efficient to compute it leads to excellent results even with efficient linear classifiers and it can be compressed with a minimal loss of accuracy using product quantization we report experimental results on five standard datasets pascal voc 2007 caltech 256 sun 397 ilsvrc 2010 and imagenet10k with up to 9m images and 10k classes showing that the fv framework is a state of the art patch encoding technique

iterative quantization a procrustean approach to learning binary codes for large scale image retrieval this paper addresses the problem of learning similarity preserving binary codes for efficient similarity search in large scale image collections we formulate this problem in terms of finding a rotation of zero centered data so as to minimize the quantization error of mapping this data to the vertices of a zero centered binary hypercube and propose a simple and efficient alternating minimization algorithm to accomplish this task this algorithm dubbed iterative quantization itq has connections to multiclass spectral clustering and to the orthogonal procrustes problem and it can be used both with unsupervised data embeddings such as pca and supervised embeddings such as canonical correlation analysis cca the resulting binary codes significantly outperform several other state of the art methods we also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to pca or cca finally we demonstrate an application of itq to learning binary attributes or classemes on the imagenet data set

a general framework for linear distance preserving hashing binary hashing approaches the approximate nearest neighbor search problem by transferring the data to hamming space with explicit or implicit distance preserving constraint with compact data representation binary hashing identifies the approximate nearest neighbors via very efficient hamming distance computation in this paper we propose a generic hashing framework with a new linear pairwise distance preserving objective and pointwise constraint in our framework the direct distance preserving objective aims to keep the linear relationship between the euclidean distance and the hamming distance of data points on the other hand to impose the pointwise constraint we instantiate the framework from three different perspectives with pseudo supervised unsupervised and supervised clues and obtain three different hashing methods the first one is a pseudo supervised hashing method which adopts a certain existing unsupervised hashing method to generate binary codes as pseudo supervised information for the second one we get an unsupervised hashing method by considering the quantization loss the third one as a supervised hashing method learns the hash functions in a two step paradigm furthermore we improve the above mentioned framework by constraining the global scope of the proposed linear distance preserving objective to a local range we validate our framework on four large scale benchmark data sets the experiments demonstrate that our pseudo supervised method achieves consistent improvement over the state of the art unsupervised hashing methods while our unsupervised and supervised methods achieve promising performance compared with the state of the art algorithms

fast coding of feature vectors using neighbor to neighbor search searching for matches to high dimensional vectors using hard soft vector quantization is the most computationally expensive part of various computer vision algorithms including the bag of visual word bow this paper proposes a fast computation method neighbor to neighbor ntn search 1 which skips some calculations based on the similarity of input vectors for example in image classification using dense sift descriptors the ntn search seeks similar descriptors from a point on a grid to an adjacent point applications of the ntn search to vector quantization a gaussian mixture model sparse coding and a kernel codebook for extracting image or video representation are presented in this paper we evaluated the proposed method on image and video benchmarks the pascal voc 2007 classification challenge and the trecvid 2010 semantic indexing task ntn vq reduced the coding cost by 77 4 percent and ntn gmm reduced it by 89 3 percent without any significant degradation in classification performance

scalable feature matching by dual cascaded scalar quantization for image retrieval in this paper we investigate the problem of scalable visual feature matching in large scale image search and propose a novel cascaded scalar quantization scheme in dual resolution we formulate the visual feature matching as a range based neighbor search problem and approach it by identifying hyper cubes with a dual resolution scalar quantization strategy specifically for each dimension of the pca transformed feature scalar quantization is performed at both coarse and fine resolutions the scalar quantization results at the coarse resolution are cascaded over multiple dimensions to index an image database the scalar quantization results over multiple dimensions at the fine resolution are concatenated into a binary super vector and stored into the index list for efficient verification the proposed cascaded scalar quantization csq method is free of the costly visual codebook training and thus is independent of any image descriptor training set the index structure of the csq is flexible enough to accommodate new image features and scalable to index large scale image database we evaluate our approach on the public benchmark datasets for large scale image retrieval experimental results demonstrate the competitive retrieval performance of the proposed method compared with several recent retrieval algorithms on feature quantization

linearization and input output decoupling for general nonlinear systems necessary and sufficient conditions are obtained for the linearization and input decoupling by state feedback of general nonlinear systems it is shown how these conditions can be derived from the already known conditions for affine nonlinear systems thereby also elucidating the existing theory for affine systems

detection of time and bandlimited increments and decrements in a random level noise the purpose of this study was to compare detection of increments and decrements occurring over limited regions of time and frequency within a 500 ms broadband 0 6000 hz noise three listeners tracked detection thresholds adaptively in a two interval two alternative forced choice task thresholds were measured for both increments and decrements in level l 10 log10 1 n0 n0 db where n0 is the spectral power density of the noise as a function of signal duration t 30 500 ms for a range of signal bandwidths w 62 6000 hz that were logarithmically centered around 2500 hz listeners were forced to rely on temporal and spectral profile cues for detection due to randomization of overall presentation level from interval to interval which rendered overall energy an inconsistent cue increments were detectable for all combinations of w and t whereas decrements were not consistently detectable for w 500 hz narrow band decrements were not detectable due to spread of excitation from the spectral edges of

memory for pitch versus memory for loudness the decays of pitch traces and loudness traces in short term auditory memory were compared in forced choice discrimination experiments the two stimuli presented on each trial were separated by a variable delay d they consisted of pure tones series of resolved harmonics or series of unresolved harmonics mixed with lowpass noise a roving procedure was employed in order to minimize the influence of context coding during an initial phase of each experiment frequency and intensity discrimination thresholds p c 0 80 were measured with an adaptive staircase method while d was fixed at 0 5 s the corresponding physical differences in cents or db were then constantly presented at four values of d 0 5 2 5 and 10 s in the case of intensity discrimination performance d markedly decreased when d increased from 0 5 to 2 s but was not further reduced when d was longer in the case of frequency discrimination the decline of performance as a function of d was significantly less abrupt this divergence suggests that pitch and loudness are processed in separate modules of auditory memory

modeling the shape of the scene a holistic representation of the spatial envelope in this paper we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions the procedure is based on a very low dimensional representation of the scene that we term the spatial envelope we propose a set of perceptual dimensions naturalness openness roughness expansion ruggedness that represent the dominant spatial structure of a scene then we show that these dimensions may be reliably estimated using spectral and coarsely localized information the model generates a multidimensional space in which scenes sharing membership in semantic categories e g streets highways coasts are projected closed together the performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category

spline regression hashing for fast image search techniques for fast image retrieval over large databases have attracted considerable attention due to the rapid growth of web images one promising way to accelerate image search is to use hashing technologies which represent images by compact binary codewords in this way the similarity between images can be efficiently measured in terms of the hamming distance between their corresponding binary codes although plenty of methods on generating hash codes have been proposed in recent years there are still two key points that needed to be improved 1 how to precisely preserve the similarity structure of the original data and 2 how to obtain the hash codes of the previously unseen data in this paper we propose our spline regression hashing method in which both the local and global data similarity structures are exploited to better capture the local manifold structure we introduce splines developed in sobolev space to find the local data mapping function furthermore our framework simultaneously learns the hash codes of the training data and the hash function for the unseen data which solves the out of sample problem extensive experiments conducted on real image datasets consisting of over one million images show that our proposed method outperforms the state of the art techniques

decision strategies of hearing impaired listeners in spectral shape discrimination the ability to discriminate between sounds with different spectral shapes was evaluated for normal hearing and hearing impaired listeners listeners detected a 920 hz tone added in phase to a single component of a standard consisting of the sum of five tones spaced equally on a logarithmic frequency scale ranging from 200 to 4200 hz an overall level randomization of 10 db was either present or absent in one subset of conditions the no perturbation conditions the standard stimulus was the sum of equal amplitude tones in the perturbation conditions the amplitudes of the components within a stimulus were randomly altered on every presentation for both perturbation and no perturbation conditions thresholds for the detection of the 920 hz tone were measured to compare sensitivity to changes in spectral shape between normal hearing and hearing impaired listeners to assess whether hearing impaired listeners relied on different regions of the spectrum to discriminate between sounds spectral weights were estimated from the perturbed standards by correlating the listener s responses with the level differences per component across two intervals of a two alternative forced choice task results showed that hearing impaired and normal hearing listeners had similar sensitivity to changes in spectral shape on average across frequency correlation functions also were similar for both groups of listeners suggesting that as long as all components are audible and well separated in frequency hearing impaired listeners can use information across frequency as well as normal hearing listeners analysis of the individual data revealed however that normal hearing listeners may be better able to adopt optimal weighting schemes this conclusion is only tentative as differences in internal noise may need to be considered to interpret the results obtained from weighting studies between normal hearing and hearing impaired listeners

product quantization for nearest neighbor search this paper introduces a product quantization based approach for approximate nearest neighbor search the idea is to decompose the space into a cartesian product of low dimensional subspaces and to quantize each subspace separately a vector is represented by a short code composed of its subspace quantization indices the euclidean distance between two vectors can be efficiently estimated from their codes an asymmetric version increases precision as it computes the approximate distance between a vector and a code experimental results show that our approach searches for nearest neighbors efficiently in particular in combination with an inverted file system results for sift and gist image descriptors show excellent search accuracy outperforming three state of the art approaches the scalability of our approach is validated on a data set of two billion vectors

full space local topology extraction for cross modal retrieval with the ever increasing availability of various kinds of multimedia data cross modal retrieval which enables information retrieval from various types of data given various types of query has become a research hotspot hashing based techniques have been developed to solve this problem however most previous works cannot capture the shared underlying structure of real world multimodal data which degrades their retrieval performances in this paper we propose a novel hashing method based on the extraction of the common manifold structure shared among different feature spaces to faithfully represent the common structure two kinds of local topology information are exploited in our method local angles are incorporated within the extraction of local topology of each feature space which is then used to learn a common intermediate subspace after heterogeneous features being embedded into this subspace local similarities are exploited to extract the local topology between different feature spaces and learn compact hamming embeddings to facilitate cross modal retrieval the proposed method is referred to as full space local topology extraction for hashing extensive comparisons with other state of the art methods on three benchmark multimedia data sets demonstrate the superiority of our proposed method in terms of retrieval recall and search accuracy

a sparse embedding and least variance encoding approach to hashing hashing is becoming increasingly important in large scale image retrieval for fast approximate similarity search and efficient data storage many popular hashing methods aim to preserve the knn graph of high dimensional data points in the low dimensional manifold space which is however difficult to achieve when the number of samples is big in this paper we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary to this end we partition the sample space into clusters via a linear spectral clustering method and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters this actually embeds each sample sparsely in the sample space the sparse embedding vector is employed as the feature of each sample for hashing we then propose a least variance encoding model which learns a dictionary to encode the sparse embedding feature and consequently binarize the coding coefficients as the hash codes the dictionary and the binarization threshold are jointly optimized in our model experimental results on benchmark data sets demonstrated the effectiveness of the proposed approach in comparison with state of the art methods

sensitivity to changes in overall level and spectral shape an evaluation of a channel model two experiments involving level and spectral shape discrimination which test an optimal channel model developed by durlach et al j acoust soc am 80 63 72 1986 are described the model specifies how the auditory system compares and or combines intensity information in different frequency channels in the first experiment psychometric functions were obtained for the discrimination of changes in level and discrimination of changes in spectral shape for an eight tone complex sound a variety of different base spectral shapes were tested in some conditions level randomization was introduced to reduce the reliability of across interval changes in level increasing the amount of level variation degraded performance for the level discrimination task but had no effect on the shape discrimination task in all conditions sensitivity to changes in spectral shape was superior to sensitivity to changes in level consequently two models of central noise are evaluated in an attempt to explain these results one in which central noise acts prior to the formation of the likelihood ratio and one in which central noise degrades the likelihood ratio the former model is more successful in accounting for the data in a second experiment the detectability of a level increment to one component of a multitone complex was measured the frequency content of the complex was varied by systematically removing six components from a 23 component complex thresholds were measured for increments at three different signal frequencies a common trend in the data was that when there was a spectral gap directly above the signal frequency thresholds were lowest this result differs from the predictions of a simple channel model and contrasts with results presented by green and berg q j exp psychol 43a 449 458 1991

auditory profile analysis is there perceptual constancy for spectral shape for stimuli roved in frequency the ability to discriminate between two spectral shapes was investigated using a one interval two alternative forced choice procedure each stimulus consisted of seven components equally spaced on a logarithmic frequency scale frequency ratio of 1 2 between adjacent components the two spectral shapes were produced by first generating a complex each of whose components was equally loud when heard in isolation and then increasing the level of either the third or the fifth component by a certain amount to render pitch an unusable cue the center frequencies of the stimuli were randomly varied between presentations maintaining the ratio between adjacent components over a range of about three octaves 279 2074 hz the data indicate that listeners were not able to distinguish between the two types of profiles even for very large increments in level of the third and the fifth components the results suggest that there is no perceptual constancy for this type of spectral shape for stimuli roved over a sufficiently large frequency range the spectral shape cannot be perceived independently of stimulus frequency the results are related to the multi channel model applied in profile analysis tasks

compact representation of high dimensional feature vectors for large scale image recognition and retrieval in large scale visual recognition and image retrieval tasks feature vectors such as fisher vector fv or the vector of locally aggregated descriptors vlad have achieved state of the art results however the combination of the large numbers of examples and high dimensional vectors necessitates dimensionality reduction in order to reduce its storage and cpu costs to a reasonable range in spite of the popularity of various feature compression methods this paper shows that the feature dimension selection is a better choice for high dimensional fv vlad than the feature dimension compression methods e g product quantization we show that strong correlation among the feature dimensions in the fv and the vlad may not exist which renders feature selection a natural choice we also show that many dimensions in fv vlad are noise throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods to choose features we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases for visual recognition and image retrieval respectively combining with the 1 bit quantization feature selection has achieved both higher accuracy and less computational cost than feature compression methods such as product quantization on the fv and the vlad image representations

sources of variation in profile analysis i individual differences and extended training this study investigated two sources of variance in the ability to discriminate auditory profiles individual differences and extended training the goals of the study were 1 to determine the range and origins of individual differences in profile analysis and 2 to determine whether those who initially had poor sensitivity to changes in spectral shape could eventually acquire finer sensitivity profile stimuli had 11 components with equal log spacing from 200 2200 hz thresholds ranged from 1 to 25 db signal level relative to the context level across 46 listeners the correlation between spectral shape discrimination thresholds after 2000 trials and pure tone intensity discrimination thresholds was 0 36 the range of individual differences for pure tone intensity discrimination and spectral shape discrimination was about the same two groups of listeners were given extended practice on the profile task one group that showed low thresholds after an initial 2000 trials of practice and another that showed much higher initial thresholds all listeners improved during the course of the first 2000 trials of training most of the poor listeners continued to improve during 9000 trials of training individual differences in the listeners sensitivity to changes in spectral shape still existed after the extended practice

successive versus simultaneous comparison in auditory intensity discrimination the ability to discriminate between two brief sounds having identical frequency components but differing in the intensity of one or more of the components is studied the stimulus manipulations include randomizing the overall intensity of the sounds varying the number and spacing of the components and varying the interval of time between the sounds the results from the experiment in which interstimulus interval is manipulated clearly support a profile analysis mechanism that computes two rough spectral analyses stores a classification of these spectra in memory and compares the two stores this profile mechanism involves a simultaneous comparison of different components of the complex some remote from the signal frequency rather than a successive comparison of the difference in intensity at the signal frequency the simultaneous comparison process is more sensitive when the profile is composed of many components spaced over a wide frequency range

human action recognition in unconstrained videos by explicit motion modeling human action recognition in unconstrained videos is a challenging problem with many applications most state of the art approaches adopted the well known bag of features representations generated based on isolated local patches or patch trajectories where motion patterns such as object object and object background relationships are mostly discarded in this paper we propose a simple representation aiming at modeling these motion relationships we adopt global and local reference points to explicitly characterize motion information so that the final representation is more robust to camera movements which widely exist in unconstrained videos our approach operates on the top of visual codewords generated on dense local patch trajectories and therefore does not require foreground background separation which is normally a critical and difficult step in modeling object relationships through an extensive set of experimental evaluations we show that the proposed representation produces a very competitive performance on several challenging benchmark data sets further combining it with the standard bag of features or fisher vector representations can lead to substantial improvements

orthogonal optimal reverse prediction for semi supervised learning optimal reverse prediction orp has recently been proposed as a semi supervised framework to unify supervised and unsupervised training methods such as supervised least square principal component analysis pca k means clustering and normalized graph cut orp has an ability to deal with classification tasks in which the labeled data are insufficient but the performance of orp and its kernelized version is still not satisfactory for classification applications to further improve performance of orp motivated by recently proposed orthogonal k means clustering in this paper we propose an orthogonal optimal reverse prediction oorp together with its kernelized and laplacian regularized extensions with only limited additional computations our algorithms can greatly enhance the classification performance compared to the original orp extensive experiments on synthetic and benchmark data collections consistently prove the effectiveness and efficiency of our oorp in comparison with several competing approaches highlightswe imposed the column orthogonal constraints to the mapping operator and propose the orthogonal optimal reverse prediction algorithm which shows a better performance than the original algorithm we imposed the orthogonal constraints method to the kernelized optimal reverse prediction algorithm and proposed the kernelized orthogonal optimal reverse prediction an optimization algorithm is designed to solve the problem and the model s classification performance is encouraging we also extend the idea to the laplacian orthogonal optimal reverse prediction and get a better classification performance

defocus blur invariant scale space feature extractions we propose modifications to scale space feature extraction techniques scale invariant feature transform sift and speeded up robust features surfs that make the feature detection and description invariant to defocus blur specifically the scale space blob detection relies on the second derivative responses of images our analysis of circular defocus blur which sufficiently approximates a real camera blur kernel and its effect on scale space blob detection suggests that fourth derivative and not the usual second derivative is optimal for detecting the blurred blobs while multi scale descriptors of blurred blobs are effective at establishing correspondences between the blurred images the proposed defocus blur invariant dbi scale space feature extraction techniques which we refer to as dbi sift and dbi surf do not require image deblurring nor blur kernel estimation meaning that their accuracy does not depend on the quality of image deblurring we offer empirical evidence of blur invariance by establishing interest point correspondences between sharp or blurred reference images and blurred target images

image search with selective match kernels aggregation across single and multiple images this paper considers a family of metrics to compare images based on their local descriptors it encompasses the vector or locally aggregated descriptors descriptor and matching techniques such as hamming embedding making the bridge between these approaches leads us to propose a match kernel that takes the best of existing techniques by combining an aggregation procedure with a selective match kernel the representation underpinning this kernel is approximated providing a large scale image search both precise and scalable as shown by our experiments on several benchmarks we show that the same aggregation procedure originally applied per image can effectively operate on groups of similar features found across multiple images this method implicitly performs feature set augmentation while enjoying savings in memory requirements at the same time finally the proposed method is shown effective for place recognition outperforming state of the art methods on a large scale landmark recognition benchmark

neighborhood discriminant hashing for large scale image retrieval with the proliferation of large scale community contributed images hashing based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its computational and memory efficiency in this paper we propose a novel hashing method named neighborhood discriminant hashing ndh for short to implement approximate similarity search different from the previous work we propose to learn a discriminant hashing function by exploiting local discriminative information i e the labels of a sample can be inherited from the neighbor samples it selects the hashing function is expected to be orthogonal to avoid redundancy in the learned hashing bits as much as possible while an information theoretic regularization is jointly exploited using maximum entropy principle as a consequence the learned hashing function is compact and nonredundant among bits while each bit is highly informative extensive experiments are carried out on four publicly available data sets and the comparison results demonstrate the outperforming performance of the proposed ndh method over state of the art hashing techniques

absolute equivalence and dynamic feedback linearization abstract elie cartan s results on absolute equivalence of differential systems are shown to imply a generalization of a recent result on dynamic feedback linearization of scalar control systems

ldahash improved matching with smaller descriptors sift like local feature descriptors are ubiquitously employed in computer vision applications such as content based retrieval video analysis copy detection object recognition photo tourism and 3d reconstruction feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations in particular affine and intensity scale transformations however real transformations that an image can undergo can only be approximately modeled in this way and thus most descriptors are only approximately invariant in practice second descriptors are usually high dimensional e g sift is represented as a 128 dimensional vector in large scale retrieval and matching problems this can pose challenges in storing and retrieving descriptor data we map the descriptor vectors into the hamming space in which the hamming metric is used to compare the resulting representations this way we reduce the size of the descriptors by representing them as short binary strings and learn descriptor invariance from examples we show extensive experimental validation demonstrating the advantage of the proposed approach

batch orthogonal locality sensitive hashing for angular similarity sign random projection locality sensitive hashing srp lsh is a widely used hashing method which provides an unbiased estimate of pairwise angular similarity yet may suffer from its large estimation variance we propose in this work batch orthogonal locality sensitive hashing bolsh as a significant improvement of srp lsh instead of independent random projections bolsh makes use of batch orthogonalized random projections i e we divide random projection vectors into several batches and orthogonalize the vectors in each batch respectively these batch orthogonalized random projections partition the data space into regular regions and thus provide a more accurate estimator we prove theoretically that bolsh still provides an unbiased estimate of pairwise angular similarity with a smaller variance for any angle in 0 compared with srp lsh furthermore we give a lower bound on the reduction of variance the extensive experiments on real data well validate that with the same length of binary code bolsh may achieve significant mean squared error reduction in estimating pairwise angular similarity moreover bolsh shows the superiority in extensive approximate nearest neighbor ann retrieval experiments

mixture of subspaces image representation and compact coding for large scale image retrieval there are two major approaches to content based image retrieval using local image descriptors one is descriptor by descriptor matching and the other is based on comparison of global image representation that describes the set of local descriptors of each image in large scale problems the latter is preferred due to its smaller memory requirements however it tends to be inferior to the former in terms of retrieval accuracy to achieve both low memory cost and high accuracy we investigate an asymmetric approach in which the probability distribution of local descriptors is modeled for each individual database image while the local descriptors of a query are used as is we adopt a mixture model of probabilistic principal component analysis the model parameters constitute a global image representation to be stored in database then the likelihood function is employed to compute a matching score between each database image and a query we also propose an algorithm to encode our image representation into more compact codes experimental results demonstrate that our method can represent each database image in less than several hundred bytes achieving higher retrieval accuracy than the state of the art method using fisher vectors