mtybilly/Instruct2DS huggingface dataset:
Adding Unlabeled Samples to Categories by Learned Attributes
Authors: Mohammad Rastegari, Ali Farhadi, Larry S. Davis Jonghyun Choi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes. Our optimization formulation discovers category specific attributes as well as the images that have high confidence in terms of the attributes. In addition, we propose a method to stably capture example-specific attributes for a small sized training set. Our method adds images to a category from a large unlabeled image pool, and leads to significant improvement in category recognition accuracy evaluated on a large-scale dataset, ImageNet.
Auxiliary Cuts for General Classes of Higher Order Functionals
Authors: Lena Gorelick, Yuri Boykov Ismail Ben Ayed
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Several recent studies demonstrated that higher order (non-linear) functionals can yield outstanding performances in the contexts of segmentation, co-segmentation and tracking. In general, higher order functionals result in difficult problems that are not amenable to standard optimizers, and most of the existing works investigated particular forms of such functionals. In this study, we derive general bounds for a broad class of higher order functionals. By introducing auxiliary variables and invoking the Jensen's inequality as well as some convexity arguments, we prove that these bounds are auxiliary functionals for various non-linear terms, which include but are not limited to several affinity measures on the distributions or moments of segment appearance and shape, as well as soft constraints on segment volume. From these general-form bounds, we state various non-linear problems as the optimization of auxiliary functionals by graph cuts. The proposed bound optimizers are derivative-free, and consistently yield very steep functional decreases, thereby converging within a few graph cuts. We report several experiments on color and medical data, along with quantitative comparisons to stateof-the-art methods. The results demonstrate competitive performances of the proposed algorithms in regard to accuracy and convergence speed, and confirm their potential in various vision and medical applications.
Template-Based Isometric Deformable 3D Reconstruction with Sampling-Based Focal Length Self-Calibration
Authors: Toby Collins Adrien Bartoli
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: It has been shown that a surface deforming isometrically can be reconstructed from a single image and a template 3D shape. Methods from the literature solve this problem efficiently. However, they all assume that the camera model is calibrated, which drastically limits their applicability. We propose (i) a general variational framework that applies to (calibrated and uncalibrated) general camera models and (ii) self-calibrating 3D reconstruction algorithms for the weak-perspective and full-perspective camera models. In the former case, our algorithm returns the normal field and camera's scale factor. In the latter case, our algorithm returns the normal field, depth and camera's focal length. Our algorithms are the first to achieve deformable 3D reconstruction including camera self-calibration. They apply to much more general setups than existing methods. Experimental results on simulated and real data show that our algorithms give results with the same level of accuracy as existing methods (which use the true focal length) on perspective images, and correctly find the normal field on affine images for which the existing methods fail.
Binary Code Ranking with Weighted Hamming Distance
Authors: Yongdong Zhang, Jinhu Tang, Ke Lu, Qi Tian Lei Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Binary hashing has been widely used for efficient similarity search due to its query and storage efficiency. In most existing binary hashing methods, the high-dimensional data are embedded into Hamming space and the distance or similarity of two points are approximated by the Hamming distance between their binary codes. The Hamming distance calculation is efficient, however, in practice, there are often lots of results sharing the same Hamming distance to a query, which makes this distance measure ambiguous and poses a critical issue for similarity search where ranking is important. In this paper, we propose a weighted Hamming distance ranking algorithm (WhRank) to rank the binary codes of hashing methods. By assigning different bit-level weights to different hash bits, the returned binary codes are ranked at a finer-grained binary code level. We give an algorithm to learn the data-adaptive and query-sensitive weight for each hash bit. Evaluations on two large-scale image data sets demonstrate the efficacy of our weighted Hamming distance for binary code ranking.
Video Editing with Temporal, Spatial and Appearance Consistency
Authors: Xiaochun Cao, Xiaowu Chen, Yi Ma Xiaojie Guo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Given an area of interest in a video sequence, one may want to manipulate or edit the area, e.g. remove occlusions from or replace with an advertisement on it. Such a task involves three main challenges including temporal consistency, spatial pose, and visual realism. The proposed method effectively seeks an optimal solution to simultaneously deal with temporal alignment, pose rectification, as well as precise recovery of the occlusion. To make our method applicable to long video sequences, we propose a batch alignment method for automatically aligning and rectifying a small number of initial frames, and then show how to align the remaining frames incrementally to the aligned base images. From the error residual of the robust alignment process, we automatically construct a trimap of the region for each frame, which is used as the input to alpha matting methods to extract the occluding foreground. Experimental results on both simulated and real data demonstrate the accurate and robust performance of our method.
Unsupervised Joint Object Discovery and Segmentation in Internet Images
Authors: Armand Joulin, Johannes Kopf, Ce Liu Michael Rubinstein
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a new unsupervised algorithm to discover and segment out common objects from large and diverse image collections. In contrast to previous co-segmentation methods, our algorithm performs well even in the presence of significant amounts of noise images (images not containing a common object), as typical for datasets collected from Internet search. The key insight to our algorithm is that common object patterns should be salient within each image, while being sparse with respect to smooth transformations across images. We propose to use dense correspondences between images to capture the sparsity and visual variability of the common object over the entire database, which enables us to ignore noise objects that may be salient within their own images but do not commonly occur in others. We performed extensive numerical evaluation on established co-segmentation datasets, as well as several new datasets generated using Internet search. Our approach is able to effectively segment out the common object for diverse object categories, while naturally identifying images where the common object is not present.
Learning SURF Cascade for Fast and Accurate Object Detection
Authors: Yimin Zhang Jianguo Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper presents a novel learning framework for training boosting cascade based object detector from large scale dataset. The framework is derived from the wellknown Viola-Jones (VJ) framework but distinguished by three key differences. First, the proposed framework adopts multi-dimensional SURF features instead of single dimensional Haar features to describe local patches. In this way, the number of used local patches can be reduced from hundreds of thousands to several hundreds. Second, it adopts logistic regression as weak classifier for each local patch instead of decision trees in the VJ framework. Third, we adopt AUC as a single criterion for the convergence test during cascade training rather than the two trade-off criteria (false-positive-rate and hit-rate) in the VJ framework. The benefit is that the false-positive-rate can be adaptive among different cascade stages, and thus yields much faster convergence speed of SURF cascade. Combining these points together, the proposed approach has three good properties. First, the boosting cascade can be trained very efficiently. Experiments show that the proposed approach can train object detectors from billions of negative samples within one hour even on personal computers. Second, the built detector is comparable to the stateof-the-art algorithm not only on the accuracy but also on the processing speed. Third, the built detector is small in model-size due to short cascade stages.
Efficient Computation of Shortest Path-Concavity for 3D Meshes
Authors: Marcel Campen, Leif Kobbelt Henrik Zimmer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In the context of shape segmentation and retrieval object-wide distributions of measures are needed to accurately evaluate and compare local regions of shapes. Lien et al. [16] proposed two point-wise concavity measures in the context of Approximate Convex Decompositions of polygons measuring the distance from a point to the polygon's convex hull: an accurate Shortest Path-Concavity (SPC) measure and a Straight Line-Concavity (SLC) approximation of the same. While both are practicable on 2D shapes, the exponential costs of SPC in 3D makes it inhibitively expensive for a generalization to meshes [14]. In this paper we propose an efficient and straight forward approximation of the Shortest Path-Concavity measure to 3D meshes. Our approximation is based on discretizing the space between mesh and convex hull, thereby reducing the continuous Shortest Path search to an efficiently solvable graph problem. Our approach works outof-the-box on complex mesh topologies and requires no complicated handling of genus. Besides presenting a rigorous evaluation of our method on a variety of input meshes, we also define an SPC-based Shape Descriptor and show its superior retrieval and runtime performance compared with the recently presented results on the Convexity Distribution by Lian et al. [12].
Learning Discriminative Illumination and Filters for Raw Material Classification with Optimal Projections of Bidirectional Texture Functions
Authors: Geifei Yang, Jinwei Gu Chao Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a computational imaging method for raw material classification using features of Bidirectional Texture Functions (BTF). Texture is an intrinsic feature for many materials, such as wood, fabric, and granite. At appropriate scales, even "uniform" materials will also exhibit texture features that can be helpful for recognition, such as paper, metal, and ceramic. To cope with the high-dimensionality of BTFs, in this paper, we proposed to learn discriminative illumination patterns and texture filters, with which we can directly measure optimal projections of BTFs for classification. We also studied the effects of texture rotation and scale variation for material classification. We built an LED-based multispectral dome, with which we have acquired a BTF database of a variety of materials and demonstrated the effectiveness of the proposed approach for material classification.
Illumination Estimation Based on Bilayer Sparse Coding
Authors: Weihua Xiong, Weiming Hu, Houwen Peng Bing Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Computational color constancy is a very important topic in computer vision and has attracted many researchers' attention. Recently, lots of research has shown the effects of using high level visual content cues for improving illumination estimation. However, nearly all the existing methods are essentially combinational strategies in which image's content analysis is only used to guide the combination or selection from a variety of individual illumination estimation methods. In this paper, we propose a novel bilayer sparse coding model for illumination estimation that considers image similarity in terms of both low level color distribution and high level image scene content simultaneously. For the purpose, the image's scene content information is integrated with its color distribution to obtain optimal illumination estimation model. The experimental results on real-world image sets show that our algorithm is superior to some prevailing illumination estimation methods, even better than some combinational methods.
Leveraging Structure from Motion to Learn Discriminative Codebooks for Scalable Landmark Classification
Authors: Sudipta N. Sinha, Lorenzo Torresani Alessandro Bergamo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper we propose a new technique for learning a discriminative codebook for local feature descriptors, specifically designed for scalable landmark classification. The key contribution lies in exploiting the knowledge of correspondences within sets of feature descriptors during codebook learning. Feature correspondences are obtained using structure from motion (SfM) computation on Internet photo collections which serve as the training data. Our codebook is defined by a random forest that is trained to map corresponding feature descriptors into identical codes. Unlike prior forest-based codebook learning methods, we utilize fine-grained descriptor labels and address the challenge of training a forest with an extremely large number of labels. Our codebook is used with various existing feature encoding schemes and also a variant we propose for importanceweighted aggregation of local features. We evaluate our approach on a public dataset of 25 landmarks and our new dataset of 620 landmarks (614K images). Our approach significantly outperforms the state of the art in landmark classification. Furthermore, our method is memory efficient and scalable.
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition
Authors: Rui Cai, Zhiwei Li, Lei Zhang, Yanwei Pang, Feng Wu, Yong Rui Qiang Hao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: 3D model-based object recognition has been a noticeable research trend in recent years. Common methods find 2D-to-3D correspondences and make recognition decisions by pose estimation, whose efficiency usually suffers from noisy correspondences caused by the increasing number of target objects. To overcome this scalability bottleneck, we propose an efficient 2D-to-3D correspondence filtering approach, which combines a light-weight neighborhoodbased step with a finer-grained pairwise step to remove spurious correspondences based on 2D/3D geometric cues. On a dataset of 300 3D objects, our solution achieves ~10 times speed improvement over the baseline, with a comparable recognition accuracy. A parallel implementation on a quad-core CPU can run at ~3fps for 1280x720 images.
Weakly Supervised Learning for Attribute Localization in Outdoor Scenes
Authors: Jungseock Joo, Yizhou Wang, Song-Chun Zhu Shuo Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we propose a weakly supervised method for simultaneously learning scene parts and attributes from a collection of images associated with attributes in text, where the precise localization of the each attribute left unknown. Our method includes three aspects. (i) Compositional scene configuration. We learn the spatial layouts of the scene by Hierarchical Space Tiling (HST) representation, which can generate an excessive number of scene configurations through the hierarchical composition of a relatively small number of parts. (ii) Attribute association. The scene attributes contain nouns and adjectives corresponding to the objects and their appearance descriptions respectively. We assign the nouns to the nodes (parts) in HST using nonmaximum suppression of their correlation, then train an appearance model for each noun+adjective attribute pair. (iii) Joint inference and learning. For an image, we compute the most probable parse tree with the attributes as an instantiation of the HST by dynamic programming. Then update the HST and attribute association based on the inferred parse trees. We evaluate the proposed method by (i) showing the improvement of attribute recognition accuracy; and (ii) comparing the average precision of localizing attributes to the scene parts.
Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines
Authors: Eric P. Xing Gunhee Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: With an explosion of popularity of online photo sharing, we can trivially collect a huge number of photo streams for any interesting topics such as scuba diving as an outdoor recreational activity class. Obviously, the retrieved photo streams are neither aligned nor calibrated since they are taken in different temporal, spatial, and personal perspectives. However, at the same time, they are likely to share common storylines that consist of sequences of events and activities frequently recurred within the topic. In this paper, as a first technical step to detect such collective storylines, we propose an approach to jointly aligning and segmenting uncalibrated multiple photo streams. The alignment task discovers the matched images between different photo streams, and the image segmentation task parses each image into multiple meaningful regions to facilitate the image understanding. We close a loop between the two tasks so that solving one task helps enhance the performance of the other in a mutually rewarding way. To this end, we design a scalable message-passing based optimization framework to jointly achieve both tasks for the whole input image set at once. With evaluation on the new Flickr dataset of 15 outdoor activities that consist of 1.5 millions of images of 13 thousands of photo streams, our empirical results show that the proposed algorithms are more successful than other candidate methods for both tasks.
Studying Relationships between Human Gaze, Description, and Computer Vision
Authors: Yifan Peng, Dimitris Samaras, Gregory J. Zelinsky, Tamara L. Berg Kiwon Yun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We posit that user behavior during natural viewing of images contains an abundance of information about the content of images as well as information related to user intent and user defined content importance. In this paper, we conduct experiments to better understand the relationship between images, the eye movements people make while viewing images, and how people construct natural language to describe images. We explore these relationships in the context of two commonly used computer vision datasets. We then further relate human cues with outputs of current visual recognition systems and demonstrate prototype applications for gaze-enabled detection and annotation.
SLAM++: Simultaneous Localisation and Mapping at the Level of Objects
Authors: Richard A. Newcombe, Hauke Strasdat, Paul H.J. Kelly, Andrew J. Davison Renato F. Salas-Moreno
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present the major advantages of a new 'object oriented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, realtime 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually refined by efficient pose-graph optimisation. This offers the descriptive and predictive power of SLAM systems which perform dense surface reconstruction, but with a huge representation compression. The object graph enables predictions for accurate ICP-based camera to model tracking at each live frame, and efficient active search for new objects in currently undescribed image regions. We demonstrate real-time incremental SLAM in large, cluttered environments, including loop closure, relocalisation and the detection of moved objects, and of course the generation of an object level scene description with the potential to enable interaction.
A Theory of Refractive Photo-Light-Path Triangulation
Authors: Peter Sturm Visesh Chari
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: 3D reconstruction of transparent refractive objects like a plastic bottle is challenging: they lack appearance related visual cues and merely reflect and refract light from the surrounding environment. Amongst several approaches to reconstruct such objects, the seminal work of Light-Path triangulation [17] is highly popular because of its general applicability and analysis of minimal scenarios. A lightpath is defined as the piece-wise linear path taken by a ray of light as it passes from source, through the object and into the camera. Transparent refractive objects not only affect the geometric configuration of light-paths but also their radiometric properties. In this paper, we describe a method that combines both geometric and radiometric information to do reconstruction. We show two major consequences of the addition of radiometric cues to the light-path setup. Firstly, we extend the case of scenarios in which reconstruction is plausible while reducing the minimal requirements for a unique reconstruction. This happens as a consequence of the fact that radiometric cues add an additional known variable to the already existing system of equations. Secondly, we present a simple algorithm for reconstruction, owing to the nature of the radiometric cue. We present several synthetic experiments to validate our theories, and show high quality reconstructions in challenging scenarios.
Learning Structured Low-Rank Representations for Image Classification
Authors: Zhuolin Jiang, Larry S. Davis Yangmuzi Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: An approach to learn a structured low-rank representation for image classification is presented. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier. Experimental results demonstrate the effectiveness of our approach.
Detecting and Aligning Faces by Image Retrieval
Authors: Zhe Lin, Jonathan Brandt, Ying Wu Xiaohui Shen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods[24] due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplarbased face detector that integrates image retrieval and discriminative learning. A large database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. A voting-based method is then proposed to let these classifiers cast votes on the test image through an efficient image retrieval technique. As a result, faces can be very efficiently detected by selecting the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. Moreover, due to the exemplar-based framework, our approach can detect faces under challenging conditions without explicitly modeling their variations. Evaluation on two public benchmark datasets shows that our new face detection approach is accurate and efficient, and achieves the state-of-the-art performance. We further propose to use image retrieval for face validation (in order to remove false positives) and for face alignment/landmark localization. The same methodology can also be easily generalized to other facerelated tasks, such as attribute recognition, as well as general object detection.
Towards Contactless, Low-Cost and Accurate 3D Fingerprint Identification
Authors: Cyril Kwong Ajay Kumar
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In order to avail the benefits of higher user convenience, hygiene, and improved accuracy, contactless 3D fingerprint recognition techniques have recently been introduced. One of the key limitations of these emerging 3D fingerprint technologies to replace the conventional 2D fingerprint system is their bulk and high cost, which mainly results from the use of multiple imaging cameras or structured lighting employed in these systems. This paper details the development of a contactless 3D fingerprint identification system that uses only single camera. We develop a new representation of 3D finger surface features using Finger Surface Codes and illustrate its effectiveness in matching 3D fingerprints. Conventional minutiae representation is extended in 3D space to accurately match the recovered 3D minutiae. Multiple 2D fingerprint images (with varying illumination profile) acquired to build 3D fingerprints can themselves be used recover 2D features for further improving 3D fingerprint identification and has been illustrated in this paper. The experimental results are shown on a database of 240 client fingerprints and confirm the advantages of the single camera based 3D fingerprint identification.
Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling
Authors: Kihyuk Sohn, Honglak Lee, Erik Learned-Miller Andrew Kae
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Conditional random fields (CRFs) provide powerful tools for building models to label image segments. They are particularly well-suited to modeling local interactions among adjacent regions (e.g., superpixels). However, CRFs are limited in dealing with complex, global (long-range) interactions between regions. Complementary to this, restricted Boltzmann machines (RBMs) can be used to model global shapes produced by segmentation models. In this work, we present a new model that uses the combined power of these two network types to build a state-of-the-art labeler. Although the CRF is a good baseline labeler, we show how an RBM can be added to the architecture to provide a global shape bias that complements the local modeling provided by the CRF. We demonstrate its labeling performance for the parts of complex face images from the Labeled Faces in the Wild data set. This hybrid model produces results that are both quantitatively and qualitatively better than the CRF alone. In addition to high-quality labeling results, we demonstrate that the hidden units in the RBM portion of our model can be interpreted as face attributes that have been learned without any attribute-level supervision.
It's Not Polite to Point: Describing People with Uncertain Attributes
Authors: Andrew Gallagher, Tsuhan Chen Amir Sadovnik
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Visual attributes are powerful features for many different applications in computer vision such as object detection and scene recognition. Visual attributes present another application that has not been examined as rigorously: verbal communication from a computer to a human. Since many attributes are nameable, the computer is able to communicate these concepts through language. However, this is not a trivial task. Given a set of attributes, selecting a subset to be communicated is task dependent. Moreover, because attribute classifiers are noisy, it is important to find ways to deal with this uncertainty. We address the issue of communication by examining the task of composing an automatic description of a person in a group photo that distinguishes him from the others. We introduce an efficient, principled method for choosing which attributes are included in a short description to maximize the likelihood that a third party will correctly guess to which person the description refers. We compare our algorithm to computer baselines and human describers, and show the strength of our method in creating effective descriptions.
Reconstructing Loopy Curvilinear Structures Using Integer Programming
Authors: Fethallah Benmansour, Bjoern Andres, Hanspeter Pfister, Pascal Fua Engin Turetken
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose a novel approach to automated delineation of linear structures that form complex and potentially loopy networks. This is in contrast to earlier approaches that usually assume a tree topology for the networks. At the heart of our method is an Integer Programming formulation that allows us to find the global optimum of an objective function designed to allow cycles but penalize spurious junctions and early terminations. We demonstrate that it outperforms state-of-the-art techniques on a wide range of datasets.
Weakly-Supervised Dual Clustering for Image Semantic Segmentation
Authors: Jing Liu, Zechao Li, Jinhui Tang, Hanqing Lu Yang Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we propose a novel Weakly-Supervised Dual Clustering (WSDC) approach for image semantic segmentation with image-level labels, i.e., collaboratively performing image segmentation and tag alignment with those regions. The proposed approach is motivated from the observation that superpixels belonging to an object class usually exist across multiple images and hence can be gathered via the idea of clustering. In WSDC, spectral clustering is adopted to cluster the superpixels obtained from a set of over-segmented images. At the same time, a linear transformation between features and labels as a kind of discriminative clustering is learned to select the discriminative features among different classes. The both clustering outputs should be consistent as much as possible. Besides, weakly-supervised constraints from image-level labels are imposed to restrict the labeling of superpixels. Finally, the non-convex and non-smooth objective function are efficiently optimized using an iterative CCCP procedure. Extensive experiments conducted on MSRC and LabelMe datasets demonstrate the encouraging performance of our method in comparison with some state-of-the-arts.
Multi-target Tracking by Rank-1 Tensor Approximation
Authors: Haibin Ling, Junling Xing, Weiming Hu Xinchu Shi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper we formulate multi-target tracking (MTT) as a rank-1 tensor approximation problem and propose an 1 norm tensor power iteration solution. In particular, a high order tensor is constructed based on trajectories in the time window, with each tensor element as the affinity of the corresponding trajectory candidate. The local assignment variables are the 1 normalized vectors, which are used to approximate the rank-1 tensor. Our approach provides a flexible and effective formulation where both pairwise and high-order association energies can be used expediently. We also show the close relation between our formulation and the multi-dimensional assignment (MDA) model. To solve the optimization in the rank-1 tensor approximation, we propose an algorithm that iteratively powers the intermediate solution followed by an 1 normalization. Aside from effectively capturing high-order motion information, the proposed solver runs efficiently with proved convergence. The experimental validations are conducted on two challenging datasets and our method demonstrates promising performances on both.
Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior
Authors: David Wipf, Yanning Zhang Haichao Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper presents a robust algorithm for estimating a single latent sharp image given multiple blurry and/or noisy observations. The underlying multi-image blind deconvolution problem is solved by linking all of the observations together via a Bayesian-inspired penalty function which couples the unknown latent image, blur kernels, and noise levels together in a unique way. This coupled penalty function enjoys a number of desirable properties, including a mechanism whereby the relative-concavity or shape is adapted as a function of the intrinsic quality of each blurry observation. In this way, higher quality observations may automatically contribute more to the final estimate than heavily degraded ones. The resulting algorithm, which requires no essential tuning parameters, can recover a high quality image from a set of observations containing potentially both blurry and noisy examples, without knowing a priori the degradation type of each observation. Experimental results on both synthetic and real-world test images clearly demonstrate the efficacy of the proposed method.
Templateless Quasi-rigid Shape Modeling with Implicit Loop-Closure
Authors: Jiaxiang Zheng, Xuan Cheng, Xinguo Liu Ming Zeng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper presents a method for quasi-rigid objects modeling from a sequence of depth scans captured at different time instances. As quasi-rigid objects, such as human bodies, usually have shape motions during the capture procedure, it is difficult to reconstruct their geometries. We represent the shape motion by a deformation graph, and propose a model-to-part method to gradually integrate sampled points of depth scans into the deformation graph. Under an as-rigid-as-possible assumption, the model-to-part method can adjust the deformation graph non-rigidly, so as to avoid error accumulation in alignment, which also implicitly achieves loop-closure. To handle the drift and topological error for the deformation graph, two algorithms are introduced. First, we use a two-stage registration to largely keep the rigid motion part. Second, in the step of graph integration, we topology-adaptively integrate new parts and dynamically control the regularization effect of the deformation graph. We demonstrate the effectiveness and robustness of our method by several depth sequences of quasi-rigid objects, and an application in human shape modeling.
Cross-View Action Recognition via a Continuous Virtual Path
Authors: Chunheng Wang, Baihua Xiao, Wen Zhou, Shuang Liu, Cunzhao Shi Zhong Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we propose a novel method for cross-view action recognition via a continuous virtual path which connects the source view and the target view. Each point on this virtual path is a virtual view which is obtained by a linear transformation of the action descriptor. All the virtual views are concatenated into an infinite-dimensional feature to characterize continuous changes from the source to the target view. However, these infinite-dimensional features cannot be used directly. Thus, we propose a virtual view kernel to compute the value of similarity between two infinite-dimensional features, which can be readily used to construct any kernelized classifiers. In addition, there are a lot of unlabeled samples from the target view, which can be utilized to improve the performance of classifiers. Thus, we present a constraint strategy to explore the information contained in the unlabeled samples. The rationality behind the constraint is that any action video belongs to only one class. Our method is verified on the IXMAS dataset, and the experimental results demonstrate that our method achieves better performance than the state-of-the-art methods.
Non-rigid Structure from Motion with Diffusion Maps Prior
Authors: Bogdan J. Matuszewski Lili Tao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, a novel approach based on a non-linear manifold learning technique is proposed to recover 3D nonrigid structures from 2D image sequences captured by a single camera. Most of the existing approaches assume that 3D shapes can be accurately modelled in a linear subspace. These techniques perform well when the deformations are relatively small or simple, but fail when more complex deformations need to be recovered. The non-linear deformations are often observed in highly flexible objects for which the use of the linear model is impractical. A specific type of shape variations might be governed by only a small number of parameters, therefore can be wellrepresented in a low dimensional manifold. We learn a nonlinear shape prior using diffusion maps method. The key
Discriminative Non-blind Deblurring
Authors: Carsten Rother, Sebastian Nowozin, Jeremy Jancsary, Stefan Roth Uwe Schmidt
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are computationally expensive. To this date, manually-defined models are thus most widely used, though limiting the attained restoration quality. We address this gap by proposing a discriminative approach for non-blind deblurring. One key challenge is that the blur kernel in use at test time is not known in advance. To address this, we analyze existing approaches that use half-quadratic regularization. From this analysis, we derive a discriminative model cascade for image deblurring. Our cascade model consists of a Gaussian CRF at each stage, based on the recently introduced regression tree fields. We train our model by loss minimization and use synthetically generated blur kernels to generate training data. Our experiments show that the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur.
Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso
Authors: Shu Liao, Yaozong Gao, Daoqiang Zhang, Yang Gao, Dinggang Shen Yinghuan Shi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Accurate prostate segmentation in CT images is a significant yet challenging task for image guided radiotherapy. In this paper, a novel semi-automated prostate segmentation method is presented. Specifically, to segment the prostate in the current treatment image, the physician first takes a few seconds to manually specify the first and last slices of the prostate in the image space. Then, the prostate is segmented automatically by the proposed two steps: (i) The first step of prostate-likelihood estimation to predict the prostate likelihood for each voxel in the current treatment image, aiming to generate the 3-D prostate-likelihood map by the proposed Spatial-COnstrained Transductive LassO (SCOTO); (ii) The second step of multi-atlases based label fusion to generate the final segmentation result by using the prostate shape information obtained from the planning and previous treatment images. The experimental result shows that the proposed method outperforms several state-of-the-art methods on prostate segmentation in a real prostate CT dataset, consisting of 24 patients with 330 images. Moreover, it is also clinically feasible since our method just requires the physician to spend a few seconds on manual specification of the first and last slices of the prostate.
Optimized Pedestrian Detection for Multiple and Occluded People
Authors: Robert T. Collins Sitapa Rujikietgumjorn
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a quadratic unconstrained binary optimization (QUBO) framework for reasoning about multiple object detections with spatial overlaps. The method maximizes an objective function composed of unary detection confidence scores and pairwise overlap constraints to determine which overlapping detections should be suppressed, and which should be kept. The framework is flexible enough to handle the problem of detecting objects as a shape covering of a foreground mask, and to handle the problem of filtering confidence weighted detections produced by a traditional sliding window object detector. In our experiments, we show that our method outperforms two existing state-ofthe-art pedestrian detectors.
Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination
Authors: Stephane Mallat Laurent Sifre
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: An affine invariant representation is constructed with a cascade of invariants, which preserves information for classification. A joint translation and rotation invariant representation of image patches is calculated with a scattering transform. It is implemented with a deep convolution network, which computes successive wavelet transforms and modulus non-linearities. Invariants to scaling, shearing and small deformations are calculated with linear operators in the scattering domain. State-of-the-art classification results are obtained over texture databases with uncontrolled viewing conditions.
A Minimum Error Vanishing Point Detection Approach for Uncalibrated Monocular Images of Man-Made Environments
Authors: Sangmin Oh, Anthony Hoogs Yiliang Xu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a novel vanishing point detection algorithm for uncalibrated monocular images of man-made environments. We advance the state-of-the-art by a new model of measurement error in the line segment extraction and minimizing its impact on the vanishing point estimation. Our contribution is twofold: 1) Beyond existing hand-crafted models, we formally derive a novel consistency measure, which captures the stochastic nature of the correlation between line segments and vanishing points due to the measurement error, and use this new consistency measure to improve the line segment clustering. 2) We propose a novel minimum error vanishing point estimation approach by optimally weighing the contribution of each line segment pair in the cluster towards the vanishing point estimation. Unlike existing works, our algorithm provides an optimal solution that minimizes the uncertainty of the vanishing point in terms of the trace of its covariance, in a closed-form. We test our algorithm and compare it with the state-of-the-art on two public datasets: York Urban Dataset and Eurasian Cities Dataset. The experiments show that our approach outperforms the state-of-the-art.
Poselet Key-Framing: A Model for Human Activity Recognition
Authors: Leonid Sigal Michalis Raptis
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we develop a new model for recognizing human actions. An action is modeled as a very sparse sequence of temporally local discriminative keyframes collections of partial key-poses of the actor(s), depicting key states in the action sequence. We cast the learning of keyframes in a max-margin discriminative framework, where we treat keyframes as latent variables. This allows us to (jointly) learn a set of most discriminative keyframes while also learning the local temporal context between them. Keyframes are encoded using a spatially-localizable poselet-like representation with HoG and BoW components learned from weak annotations; we rely on structured SVM formulation to align our components and mine for hard negatives to boost localization performance. This results in a model that supports spatio-temporal localization and is insensitive to dropped frames or partial observations. We show classification performance that is competitive with the state of the art on the benchmark UT-Interaction dataset and illustrate that our model outperforms prior methods in an on-line streaming setting.
Probabilistic Label Trees for Efficient Large Scale Image Classification
Authors: Fereshteh Sadeghi, Marshall Tappen, Ohad Shamir, Ce Liu Baoyuan Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Large-scale recognition problems with thousands of classes pose a particular challenge because applying the classifier requires more computation as the number of classes grows. The label tree model integrates classification with the traversal of the tree so that complexity grows logarithmically. In this paper, we show how the parameters of the label tree can be found using maximum likelihood estimation. This new probabilistic learning technique produces a label tree with significantly improved recognition accuracy.