pwc-archive/papers-with-abstracts huggingface dataset:
Low-Rank Tensor Constrained Multiview Subspace Clustering
Authors: Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, Xiaochun Cao
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering

In this paper, we explore the problem of multiview subspace clustering. We introduce a low-rank tensor constraint to explore the complementary information from multiple views and, accordingly, establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering (LT-MSC). Our method regards the subspace representation matrices of different views as a tensor, which captures dexterously the high order correlations underlying multiview data. Then the tensor is equipped with a low-rank constraint, which models elegantly the cross information among different views, reduces effectually the redundancy of the learned subspace representations, and improves the accuracy of clustering as well. The inference process of the affinity matrix for clustering is formulated as a tensor nuclear norm minimization problem, constrained with an additional L2,1-norm regularizer and some linear equalities. The minimization problem is convex and thus can be solved efficiently by an Augmented Lagrangian Alternating Direction Minimization (AL-ADM) method. Extensive experimental results on four benchmark datasets show the effectiveness of our proposed LT-MSC method.
BodyPrint: Pose Invariant 3D Shape Matching of Human Bodies
Authors: Jiangping Wang, Kai Ma, Vivek Kumar Singh, Thomas Huang, Terrence Chen
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology. We address this problem by proposing a novel holistic human body shape descriptor called BodyPrint. To compute the bodyprint for a given body scan, we fit a deformable human body mesh and project the mesh parameters to a low-dimensional subspace which improves discriminability across different persons. Experiments are carried out on three real-world human body datasets to demonstrate that BodyPrint is robust to pose variation as well as missing information and sensor noise. It improves the matching accuracy significantly compared to conventional 3D shape matching techniques using local features. To facilitate practical applications where the shape database may grow over time, we also extend our learning framework to handle online updates.
The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals
Authors: Ahmad Humayun, Fuxin Li, James M. Rehg
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Object proposals have recently fueled the progress in detection performance. These proposals aim to provide category-agnostic localizations for all objects in an image. One way to generate proposals is to perform parametric min-cuts over seed locations. This paper demonstrates that standard parametric-cut models are ineffective in obtaining medium-sized objects, which we refer to as the middle child problem. We propose a new energy minimization framework incorporating geodesic distances between segments which solves this problem. In addition, we introduce a new superpixel merging algorithm which can generate a small set of seeds that reliably cover a large number of objects of all sizes. We call our method POISE--- "Proposals for Objects from Improved Seeds and Energies." POISE enables parametric min-cuts to reach their full potential. On PASCAL VOC it generates  2,640 segments with an average overlap of 0.81, whereas the closest competing methods require more than 4,200 proposals to reach the same accuracy. We show detailed quantitative comparisons against 5 state-of-the-art methods on PASCAL VOC and Microsoft COCO segmentation challenges.
Contour Guided Hierarchical Model for Shape Matching
Authors: Yuanqi Su, Yuehu Liu, Bonan Cuan, Nanning Zheng
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: model

For its simplicity and effectiveness, star model is popular in shape matching. However, it suffers from the loose geometric connections among parts. In the paper, we present a novel algorithm that reconsiders these connections and reduces the global matching to a set of interrelated local matching. For the purpose, we divide the shape template into overlapped parts and model the matching through a part-based layered structure that uses the latent variable to constrain parts' deformation.  As for inference, each part is used for localizing candidates by the partial matching. Thanks to the contour fragments, the partial matching can be solved via modified dynamic programming. The overlapped regions among parts of the template are then explored to make the candidates of parts meet at their shared points. The process is fulfilled via a refined procedure based on iterative dynamic programming. Results on ETHZ shape and Inria Horse datasets demonstrate the benefits of the proposed algorithm.
Robust Image Segmentation Using Contour-Guided Color Palettes
Authors: Xiang Fu, Chien-Yi Wang, Chen Chen, Changhu Wang, C. -C. Jay Kuo
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The contour-guided color palette (CCP) is proposed for robust image segmentation. It efficiently integrates contour and color cues of an image. To find representative colors of an image, color samples along long contours between regions, similar in spirit to machine learning methodology that focus on samples near decision boundaries, are collected followed by the mean-shift (MS) algorithm in the sampled color space to achieve an image-dependent color palette. This color palette provides a preliminary segmentation in the spatial domain, which is further fine-tuned by post-processing techniques such as leakage avoidance, fake boundary removal, and small region mergence. Segmentation performances of CCP and MS are compared and analyzed. While CCP offers an acceptable standalone segmentation result, it can be further integrated into the framework of layered spectral segmentation to produce a more robust segmentation. The superior performance of CCP-based segmentation algorithm is demonstrated by experiments on the Berkeley Segmentation Dataset.
Joint Optimization of Segmentation and Color Clustering
Authors: Ekaterina Lobacheva, Olga Veksler, Yuri Boykov
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering, Segmentation

Binary energy optimization is a popular approach for segmenting a color image into foreground/background regions. To model the appearance of the regions, color, a relatively high dimensional feature, should be handled effectively. A full color histogram is usually too sparse to be reliable. One approach is to explicitly reduce dimensionality by clustering or quantizing the color space. Another popular approach is to fit GMMs for soft implicit clustering of the color space. These approaches work well when the foreground/background are sufficiently distinct. In cases of more subtle difference in appearance, both approaches may reduce or even eliminate foreground/background distinction. This happens because either color clustering is performed completely independently from the segmentation process, as a preprocessing step (in clustering), or independently for the foreground and independently for the background (in GMM). We propose to make clustering an integral part of segmentation, by including a new clustering term in the energy function. Our energy function with a clustering term favours clusterings that make foreground/background appearance more distinct. Thus our energy function jointly optimizes over color clustering, foreground/background models, and segmentation. Exact optimization is not feasible, therefore we develop an approximate algorithm. We show the advantage of including the color clustering term into the energy function on  camouflage images, as well as standard segmentation datasets.
Detection and Segmentation of 2D Curved Reflection Symmetric Structures
Authors: Ching L. Teo, Cornelia Fermuller, Yiannis Aloimonos
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Segmentation, Symmetry Detection

Symmetry, as one of the key components of Gestalt theory, provides an important mid-level cue that serves as input to higher visual processes such as segmentation. In this work, we propose a complete approach that links the detection of curved reflection symmetries to produce symmetry-constrained segments of structures/regions in real images with clutter. For curved reflection symmetry detection, we leverage on patch-based symmetric features to train a Structured Random Forest classifier that detects multiscaled curved symmetries in 2D images. Next, using these curved symmetries, we modulate a novel symmetry-constrained foreground-background segmentation by their symmetry scores so that we enforce global symmetrical consistency in the final segmentation. This is achieved by imposing a pairwise symmetry prior that encourages symmetric pixels to have the same labels over a MRF-based representation of the input image edges, and the final segmentation is obtained via graph-cuts. Experimental results over four publicly available datasets containing annotated symmetric structures: 1) SYMMAX-300, 2) BSD-Parts, 3) Weizmann Horse and 4) NY-roads demonstrate the approach's applicability to different environments with state-of-the-art performance.
Unsupervised Tube Extraction Using Transductive Learning and Dense Trajectories
Authors: Mihai Marian Puscas, Enver Sangineto, Dubravko Culibrk, Nicu Sebe
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: object-detection, Object Detection, Optical Flow Estimation, Transductive Learning

We address the problem of automatic extraction of foreground objects from videos. The goal is to provide a method for unsupervised collection of samples  which can be further used for object detection training without any human intervention. We use the well known Selective Search approach to produce an initial still-image based segmentation of the video frames.   This initial set of proposals is pruned and temporally extended using optical flow and transductive learning.    Specifically, we propose to  use Dense Trajectories in order to robustly match and track candidate boxes over different frames. The obtained box tracks are used to collect samples for  unsupervised training of track-specific detectors. Finally, the detectors are run on the videos  to extract the final tubes. The combination of appearance-based static ''objectness'' (Selective Search), motion information (Dense Trajectories) and transductive learning (detectors are forced to "overfit" on the unsupervised data used for training) makes the proposed approach extremely robust. We  outperform state-of-the-art systems by a large margin on common benchmarks used for tube proposal evaluation.
Compositional Hierarchical Representation of Shape Manifolds for Classification of Non-Manifold Shapes
Authors: Mete Ozay, Umit Rusen Aktas, Jeremy L. Wyatt, Ales Leonardis
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: General Classification

We address the problem of statistical learning of shape models which are invariant to translation, rotation and scale in compositional hierarchies when data spaces of measurements and shape spaces are not topological manifolds. In practice, this problem is observed while modeling shapes having multiple disconnected components, e.g. partially occluded shapes in cluttered scenes. We resolve the aforementioned problem by first reformulating the relationship between data and shape spaces considering the interaction between Receptive Fields (RFs) and Shape Manifolds (SMs) in a compositional hierarchical shape vocabulary. Then, we suggest a method to model the topological structure of the SMs for statistical learning of the geometric transformations of the shapes that are defined by group actions on the SMs. For this purpose, we design a disjoint union topology using an indexing mechanism for the formation of shape models on SMs in the vocabulary, recursively. We represent the topological relationship between shape components using graphs, which are aggregated to construct a hierarchical graph structure for the shape vocabulary. To this end, we introduce a framework to implement the indexing mechanisms for the employment of the vocabulary for structural shape classification. The proposed approach is used to construct invariant shape representations. Results on benchmark shape classification outperform state-of-the-art methods.
Shell PCA: Statistical Shape Modelling in Shell Space
Authors: Chao Zhang, Behrend Heeren, Martin Rumpf, William A. P. Smith
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

In this paper we describe how to perform Principal Components Analysis in "shell space". Thin shells are a physical model for surfaces with non-zero thickness whose deformation dissipates elastic energy. Thin shells, or their discrete counterparts, can be considered to reside in a shell space in which the notion of distance is given by the elastic energy required to deform one shape into another. It is in this setting that we show how to perform statistical analysis of a set of shapes (meshes in dense correspondence), providing a hybrid between physical and statistical shape modelling. The resulting models are better able to capture non-linear deformations, for example resulting from articulated motion, even when training data is very sparse compared to the dimensionality of the observation space.
Learning to Combine Mid-Level Cues for Object Proposal Generation
Authors: Tom Lee, Sanja Fidler, Sven Dickinson
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object Proposal Generation, Object Recognition

In recent years, region proposals have replaced sliding windows in support of object recognition, offering more discriminating shape and appearance information through improved localization.  One powerful approach for generating region proposals is based on minimizing parametric energy functions with parametric maxflow.  In this paper, we introduce Parametric Min-Loss (PML), a novel structured learning framework for parametric energy functions.  While PML is generally applicable to different domains, we use it in the context of region proposals to learn to combine a set of mid-level grouping cues to yield a small set of object region proposals with high recall.  Our learning framework accounts for multiple diverse outputs, and is complemented by diversification seeds based on image location and color.  This approach casts perceptual grouping and cue combination in a novel structured learning framework which yields baseline improvements on VOC 2012 and COCO 2014.
Enhancing Road Maps by Parsing Aerial Images Around the World
Authors: Gellert Mattyus, Shenlong Wang, Sanja Fidler, Raquel Urtasun
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Semantic Segmentation

In recent years, contextual models that exploit maps have been shown to be very effective for many recognition and localization tasks.  In this paper we propose to exploit aerial images in order to enhance  freely available world maps. Towards this goal, we make use of OpenStreetMap and formulate the problem as the one of inference in a Markov random field parameterized in terms of  the location of the road-segment centerlines as well as  their width.  This parameterization enables very efficient inference and  returns only topologically correct roads. In particular, we can segment all OSM roads in the  whole world in a single day using a small cluster of 10 computers. Importantly, our approach generalizes very well; it can be trained using only 1.5 km2 aerial imagery and produce very accurate results in any location across the globe.   We demonstrate the effectiveness of our approach outperforming the state-of-the-art in two new benchmarks that we collect. We then show how our enhanced maps are beneficial for semantic segmentation of ground images.
Probabilistic Appearance Models for Segmentation and Classification
Authors: Julia Kruger, Jan Ehrhardt, Heinz Handels
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Classification, General Classification, global-optimization

Statistical shape and appearance models are often based on the accurate identification of one-to-one correspondences in a training data set. At the same time, the determination of these corresponding landmarks is the most challenging part of such methods. Hufnagel etal developed an alternative method using correspondence probabilities for a statistical shape model.  We propose the use of probabilistic correspondences for statistical appearance models by incorporating appearance information into the framework. A point-based representation is employed representing the image by a set of vectors assembling position and appearances. Using probabilistic correspondences between these multi-dimensional feature vectors eliminates the need for extensive preprocessing to find corresponding landmarks and reduces the dependence of the generated model on the landmark positions. Then, a maximum a-posteriori approach is used to derive a single global optimization criterion with respect to model parameters and observation dependent parameters, that directly affects shape and appearance information of the considered structures. Model generation and fitting can be expressed by optimizing the same criterion.  The developed framework describes the modeling process in a concise and flexible mathematical way and allows for additional constraints as topological regularity in the modeling process. Furthermore, it eliminates the demand for costly correspondence determination.  We apply the model for segmentation and landmark identification in hand X-ray images, where segmentation information is modeled as further features in the vectorial image representation. The results demonstrate the feasibility of the model to reconstruct contours and landmarks for unseen test images. Furthermore, we apply the model for tissue classification, where a model is generated for healthy brain tissue using 2D MRI slices. Applying the model to images of stroke patients the probabilistic correspondences are used to classify between healthy and pathological structures. The results demonstrate the ability of the probabilistic model to recognize healthy and pathological tissue automatically.
A Randomized Ensemble Approach to Industrial CT Segmentation
Authors: Hyojin Kim, Jayaraman Jayaraman J. Thiagarajan, Peer-Timo Bremer
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Tuning the models and parameters of common segmentation approaches is challenging especially in the presence of noise and artifacts. Ensemble-based techniques attempt to compensate by randomly varying models and/or parameters to create a diverse set of hypotheses, which are subsequently ranked to arrive at the best solution. However, these methods have been restricted to cases where the underlying models are well-established, e.g. natural images. In practice, it is difficult to determine a suitable base-model and the amount of randomization required. Furthermore, for multi-object scenes no single hypothesis may perform well for all objects, reducing the overall quality of the results. This paper presents a new ensemble-based segmentation framework for industrial CT images demonstrating that comparatively simple models and randomization strategies can significantly improve the result over existing techniques. Furthermore, we introduce a per-object based ranking, followed by a consensus inference that can outperform even the best case scenario of existing hypothesis ranking approaches. We demonstrate the effectiveness of our approach using a set of noise and artifact rich CT images from baggage security and show that it significantly outperforms existing solutions in this area.
Semi-Supervised Normalized Cuts for Image Segmentation
Authors: Selene E. Chew, Nathan D. Cahill
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

Since its introduction as a  powerful graph-based method for image segmentation, the Normalized Cuts (NCuts) algorithm has been generalized to incorporate expert knowledge about how certain pixels or regions should be grouped, or how the resulting segmentation should be biased to be correlated with priors. Previous approaches incorporate hard must-link constraints on how certain pixels should be grouped as well as hard cannot-link constraints on how other pixels should be separated into different groups. In this paper, we reformulate NCuts to allow both sets of constraints to be handled in a soft manner, enabling the user to tune the degree to which the constraints are satisfied. An approximate spectral solution to the reformulated problem exists without requiring explicit construction of a large, dense matrix; hence, computation time is comparable to that of unconstrained NCuts. Using synthetic data and real imagery, we show that soft handling of constraints yields better results than unconstrained NCuts and enables more robust clustering and segmentation than is possible when the constraints are strictly enforced.
StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images
Authors: Ran Ju, Tongwei Ren, Gangshan Wu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Segmentation, Object, Segmentation, Semantic Segmentation, Stereo Matching, Stereo Matching Hand

Consistent object extraction plays an essential role for stereo image editing with the population of stereoscopic 3D media. Most previous methods perform segmentation on entire images for both views using dense stereo correspondence constraints. We find that for such kind of methods the computation is highly redundant since the two views are near-duplicate. Besides, the consistency may be violated due to the imperfectness of current stereo matching algorithms. In this paper, we propose a contour based method which searches for consistent object contours instead of regions. It integrates both stereo correspondence and object boundary constraints into an energy minimization framework. The proposed method has several advantages compared to previous works. First, the searching space is restricted in object boundaries thus the efficiency significantly improved. Second, the discriminative power of object contours results in a more consistent segmentation. Furthermore, the proposed method can effortlessly extend existing single-image segmentation methods to work in stereo scenarios. The experiment on the Adobe benchmark shows superior extraction accuracy and significant improvement of efficiency of our method to state-of-the-art. We also demonstrate in a few applications how our method can be used as a basic tool for stereo image editing.
Semantic Segmentation of RGBD Images With Mutex Constraints
Authors: Zhuo Deng, Sinisa Todorovic, Longin Jan Latecki
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Common Sense Reasoning, Scene Labeling, Scene Segmentation, Semantic Segmentation

In this paper, we address the problem of semantic scene segmentation of RGB-D images of indoor scenes. We propose a novel image region labeling method which augments CRF formulation with hard mutual exclusion (mutex) constraints. This way our approach can make use of rich and accurate 3D geometric structure coming from Kinect in a principled manner. The final labeling result must satisfy all mutex constraints, which allows us to eliminate configurations that violate common sense physics laws like placing a floor above a night stand. Three classes of mutex constraints are proposed: global object co-occurrence constraint, relative height relationship constraint, and local support relationship constraint. We evaluate our approach on the NYU-Depth V2 dataset, which consists of 1449 cluttered indoor scenes, and also test generalization of our model trained on NYU-Depth V2 dataset directly on a recent SUN3D dataset without any new training. The experimental results show that we significantly outperform the state-of-the-art methods in scene labeling on both datasets.