mtybilly/Instruct2DS huggingface dataset:
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine
Authors: Hua Wang, Feiping Nie, Heng Huang, Chris Ding
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: To better understand, search, and classify image and video information, many visual feature descriptors have been proposed to describe elementary visual characteristics, such as the shape, the color, the texture, etc. How to integrate these heterogeneous visual features and identify the important ones from them for specific vision tasks has become an increasingly critical problem. In this paper, We propose a novel Sparse Multimodal Learning (SMML) approach to integrate such heterogeneous features by using the joint structured sparsity regularizations to learn the feature importance of for the vision tasks from both group-wise and individual point of views. A new optimization algorithm is also introduced to solve the non-smooth objective with rigorously proved global convergence. We applied our SMML method to five broadly used object categorization and scene understanding image data sets for both singlelabel and multi-label image classification tasks. For each data set we integrate six different types of popularly used image features. Compared to existing scene and object categorization methods using either single modality or multimodalities of features, our approach always achieves better performances measured.
A Thousand Frames in Just a Few Words: Lingual Description of Videos through Latent Topics and Sparse Object Stitching
Authors: Pradipto Das, Chenliang Xu, Richard F. Doell, Jason J. Corso
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: The problem of describing images through natural language has gained importance in the computer vision community. Solutions to image description have either focused on a top-down approach of generating language through combinations of object detections and language models or bottom-up propagation of keyword tags from training images to test images through probabilistic or nearest neighbor techniques. In contrast, describing videos with natural language is a less studied problem. In this paper, we combine ideas from the bottom-up and top-down approaches to image description and propose a method for video description that captures the most relevant contents of a video in a natural language description. We propose a hybrid system consisting of a low level multimodal latent topic model for initial keyword annotation, a middle level of concept detectors and a high level module to produce final lingual descriptions. We compare the results of our system to human descriptions in both short and long forms on two datasets, and demonstrate that final system output has greater agreement with the human descriptions than any single level.
Online Dominant and Anomalous Behavior Detection in Videos
Authors: Mehrsan Javan Roshtkhari, Martin D. Levine
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos. Dominant behaviors are those occurring frequently in videos and hence, usually do not attract much attention. They can be characterized by different complexities in space and time, ranging from a scene background to human activities. In contrast, an anomalous behavior is defined as having a low likelihood of occurrence. We do not employ any models of the entities in the scene in order to detect these two kinds of behaviors. In this paper, video events are learnt at each pixel without supervision using densely constructed spatio-temporal video volumes. Furthermore, the volumes are organized into large contextual graphs. These compositions are employed to construct a hierarchical codebook model for the dominant behaviors. By decomposing spatio-temporal contextual information into unique spatial and temporal contexts, the proposed framework learns the models of the dominant spatial and temporal events. Thus, it is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatio-temporal pixel level changes.
Learning Class-to-Image Distance with Object Matchings
Authors: Guang-Tong Zhou, Tian Lan, Weilong Yang, Greg Mori
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We conduct image classification by learning a class-toimage distance function that matches objects. The set of objects in training images for an image class are treated as a collage. When presented with a test image, the best matching between this collage of training image objects and those in the test image is found. We validate the efficacy of the proposed model on the PASCAL 07 and SUN 09 datasets, showing that our model is effective for object classification and scene classification tasks. State-of-the-art image classification results are obtained, and qualitative results demonstrate that objects can be accurately matched.
Spectral Modeling and Relighting of Reflective-Fluorescent Scenes
Authors: Antony Lam, Imari Sato
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Hyperspectral reflectance data allows for highly accurate spectral relighting under arbitrary illumination, which is invaluable to applications ranging from archiving cultural e-heritage to consumer product design. Past methods for capturing the spectral reflectance of scenes has proven successful in relighting but they all share a common assumption. All the methods do not consider the effects of fluorescence despite fluorescence being found in many everyday objects. In this paper, we describe the very different ways that reflectance and fluorescence interact with illuminants and show the need to explicitly consider fluorescence in the relighting problem. We then propose a robust method based on well established theories of reflectance and fluorescence for imaging each of these components. Finally, we show that we can relight real scenes of reflective-fluorescent surfaces with much higher accuracy in comparison to only considering the reflective component.
Is There a Procedural Logic to Architecture?
Authors: Julien Weissenberg, Hayko Riemenschneider, Mukta Prasad, Luc Van Gool
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Urban models are key to navigation, architecture and entertainment. Apart from visualizing fac,ades, a number of tedious tasks remain largely manual (e.g. compression, generating new fac,ade designs and structurally comparing fac,ades for classification, retrieval and clustering). We propose a novel procedural modelling method to automatically learn a grammar from a set of fac,ades, generate new fac,ade instances and compare fac,ades. To deal with the difficulty of grammatical inference, we reformulate the problem. Instead of inferring a compromising, onesize-fits-all, single grammar for all tasks, we infer a model whose successive refinements are production rules tailored for each task. We demonstrate our automatic rule inference on datasets of two different architectural styles. Our method supercedes manual expert work and cuts the time required to build a procedural model of a fac,ade from several days to a few milliseconds.
Motion Estimation for Self-Driving Cars with a Generalized Camera
Authors: Gim Hee Lee, Friedrich Faundorfer, Marc Pollefeys
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we present a visual ego-motion estimation algorithm for a self-driving car equipped with a closeto-market multi-camera system. By modeling the multicamera system as a generalized camera and applying the non-holonomic motion constraint of a car, we show that this leads to a novel 2-point minimal solution for the generalized essential matrix where the full relative motion including metric scale can be obtained. We provide the analytical solutions for the general case with at least one inter-camera correspondence and a special case with only intra-camera correspondences. We show that up to a maximum of 6 solutions exist for both cases. We identify the existence of degeneracy when the car undergoes straight motion in the special case with only intra-camera correspondences where the scale becomes unobservable and provide a practical alternative solution. Our formulation can be efficiently implemented within RANSAC for robust estimation. We verify the validity of our assumptions on the motion model by comparing our results on a large real-world dataset collected by a car equipped with 4 cameras with minimal overlapping field-of-views against the GPS/INS ground truth.
Histograms of Sparse Codes for Object Detection
Authors: Xiaofeng Ren, Deva Ramanan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Object detection has seen huge progress in recent years, much thanks to the heavily-engineered Histograms of Oriented Gradients (HOG) features. Can we go beyond gradients and do better than HOG? We provide an affirmative answer by proposing and investigating a sparse representation for object detection, Histograms of Sparse Codes (HSC). We compute sparse codes with dictionaries learned from data using K-SVD, and aggregate per-pixel sparse codes to form local histograms. We intentionally keep true to the sliding window framework (with mixtures and parts) and only change the underlying features. To keep training (and testing) efficient, we apply dimension reduction by computing SVD on learned models, and adopt supervised training where latent positions of roots and parts are given externally e.g. from a HOG-based detector. By learning and using local representations that are much more expressive than gradients, we demonstrate large improvements over the state of the art on the PASCAL benchmark for both rootonly and part-based models.
Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions
Authors: Dong Zhang, Omar Javed, Mubarak Shah
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we propose a novel approach to extract primary object segments in videos in the 'object proposal' domain. The extracted primary object regions are then used to build object models for optimized video segmentation. The proposed approach has several contributions: First, a novel layered Directed Acyclic Graph (DAG) based framework is presented for detection and segmentation of the primary object in video. We exploit the fact that, in general, objects are spatially cohesive and characterized by locally smooth motion trajectories, to extract the primary object from the set of all available proposals based on motion, appearance and predicted-shape similarity across frames. Second, the DAG is initialized with an enhanced object proposal set where motion based proposal predictions (from adjacent frames) are used to expand the set of object proposals for a particular frame. Last, the paper presents a motion scoring function for selection of object proposals that emphasizes high optical flow gradients at proposal boundaries to discriminate between moving objects and the background. The proposed approach is evaluated using several challenging benchmark videos and it outperforms both unsupervised and supervised state-of-the-art methods.
Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition
Authors: Ziheng Wang, Shangfei Wang, Qiang Ji
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Spatial-temporal relations among facial muscles carry crucial information about facial expressions yet have not been thoroughly exploited. One contributing factor for this is the limited ability of the current dynamic models in capturing complex spatial and temporal relations. Existing dynamic models can only capture simple local temporal relations among sequential events, or lack the ability for incorporating uncertainties. To overcome these limitations and take full advantage of the spatio-temporal information, we propose to model the facial expression as a complex activity that consists of temporally overlapping or sequential primitive facial events. We further propose the Interval Temporal Bayesian Network to capture these complex temporal relations among primitive facial events for facial expression modeling and recognition. Experimental results on benchmark databases demonstrate the feasibility of the proposed approach in recognizing facial expressions based purely on spatio-temporal relations among facial muscles, as well as its advantage over the existing methods.
Bayesian Depth-from-Defocus with Shading Constraints
Authors: Chen Li, Shuochen Su, Yasuyuki Matsushita, Kun Zhou, Stephen Lin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a method that enhances the performance of depth-from-defocus (DFD) through the use of shading information. DFD suffers from important limitations namely coarse shape reconstruction and poor accuracy on textureless surfaces that can be overcome with the help of shading. We integrate both forms of data within a Bayesian framework that capitalizes on their relative strengths. Shading data, however, is challenging to recover accurately from surfaces that contain texture. To address this issue, we propose an iterative technique that utilizes depth information to improve shading estimation, which in turn is used to elevate depth estimation in the presence of textures. With this approach, we demonstrate improvements over existing DFD techniques, as well as effective shape reconstruction of textureless surfaces.
Sparse Output Coding for Large-Scale Visual Recognition
Authors: Bin Zhao, Eric P. Xing
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach.
Boundary Cues for 3D Object Shape Recovery
Authors: Kevin Karsch, Zicheng Liao, Jason Rock, Jonathan T. Barron, Derek Hoiem
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Early work in computer vision considered a host of geometric cues for both shape reconstruction [11] and recognition [14]. However, since then, the vision community has focused heavily on shading cues for reconstruction [1], and moved towards data-driven approaches for recognition [6]. In this paper, we reconsider these perhaps overlooked "boundary" cues (such as self occlusions and folds in a surface), as well as many other established constraints for shape reconstruction. In a variety of user studies and quantitative tasks, we evaluate how well these cues inform shape reconstruction (relative to each other) in terms of both shape quality and shape recognition. Our findings suggest many new directions for future research in shape reconstruction, such as automatic boundary cue detection and relaxing assumptions in shape from shading (e.g. orthographic projection, Lambertian surfaces).
Image Segmentation by Cascaded Region Agglomeration
Authors: Zhile Ren, Gregory Shakhnarovich
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose a hierarchical segmentation algorithm that starts with a very fine oversegmentation and gradually merges regions using a cascade of boundary classifiers. This approach allows the weights of region and boundary features to adapt to the segmentation scale at which they are applied. The stages of the cascade are trained sequentially, with asymetric loss to maximize boundary recall. On six segmentation data sets, our algorithm achieves best performance under most region-quality measures, and does it with fewer segments than the prior work. Our algorithm is also highly competitive in a dense oversegmentation (superpixel) regime under boundary-based measures.
Spatial Inference Machines
Authors: Roman Shapovalov, Dmitry Vetrov, Pushmeet Kohli
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper addresses the problem of semantic segmentation of 3D point clouds. We extend the inference machines framework of Ross et al. by adding spatial factors that model mid-range and long-range dependencies inherent in the data. The new model is able to account for semantic spatial context. During training, our method automatically isolates and retains factors modelling spatial dependencies between variables that are relevant for achieving higher prediction accuracy. We evaluate the proposed method by using it to predict 17-category semantic segmentations on sets of stitched Kinect scans. Experimental results show that the spatial dependencies learned by our method significantly improve the accuracy of segmentation. They also show that our method outperforms the existing segmentation technique of Koppula et al.
Can a Fully Unconstrained Imaging Model Be Applied Effectively to Central Cameras?
Authors: Filippo Bergamasco, Andrea Albarelli, Emanuele Rodola, Andrea Torsello
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Traditional camera models are often the result of a compromise between the ability to account for non-linearities in the image formation model and the need for a feasible number of degrees of freedom in the estimation process. These considerations led to the definition of several ad hoc models that best adapt to different imaging devices, ranging from pinhole cameras with no radial distortion to the more complex catadioptric or polydioptric optics. In this paper we propose the use of an unconstrained model even in standard central camera settings dominated by the pinhole model, and introduce a novel calibration approach that can deal effectively with the huge number of free parameters associated with it, resulting in a higher precision calibration than what is possible with the standard pinhole model with correction for radial distortion. This effectively extends the use of general models to settings that traditionally have been ruled by parametric approaches out of practical considerations. The benefit of such an unconstrained model to quasipinhole central cameras is supported by an extensive experimental validation.
Learning Compact Binary Codes for Visual Tracking
Authors: Xi Li, Chunhua Shen, Anthony Dick, Anton van den Hengel
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: A key problem in visual tracking is to represent the appearance of an object in a way that is robust to visual changes. To attain this robustness, increasingly complex models are used to capture appearance variations. However, such models can be difficult to maintain accurately and efficiently. In this paper, we propose a visual tracker in which objects are represented by compact and discriminative binary codes. This representation can be processed very efficiently, and is capable of effectively fusing information from multiple cues. An incremental discriminative learner is then used to construct an appearance model that optimally separates the object from its surrounds. Furthermore, we design a hypergraph propagation method to capture the contextual information on samples, which further improves the tracking accuracy. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.
Efficient Maximum Appearance Search for Large-Scale Object Detection
Authors: Qiang Chen, Zheng Song, Rogerio Feris, Ankur Datta, Liangliang Cao, Zhongyang Huang, Shuicheng Yan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In recent years, efficiency of large-scale object detection has arisen as an important topic due to the exponential growth in the size of benchmark object detection datasets. Most current object detection methods focus on improving accuracy of large-scale object detection with efficiency being an afterthought. In this paper, we present the Efficient Maximum Appearance Search (EMAS) model which is an order of magnitude faster than the existing state-of-the-art large-scale object detection approaches, while maintaining comparable accuracy. Our EMAS model consists of representing an image as an ensemble of densely sampled feature points with the proposed Pointwise Fisher Vector encoding method, so that the learnt discriminative scoring function can be applied locally. Consequently, the object detection problem is transformed into searching an image sub-area for maximum local appearance probability, thereby making EMAS an order of magnitude faster than the traditional detection methods. In addition, the proposed model is also suitable for incorporating global context at a negligible extra computational cost. EMAS can also incorporate fusion of multiple features, which greatly improves its performance in detecting multiple object categories. Our experiments show that the proposed algorithm can perform detection of 1000 object classes in less than one minute per image on the Image Net ILSVRC2012 dataset and for 107 object classes in less than 5 seconds per image for the SUN09 dataset using a single CPU.
A New Perspective on Uncalibrated Photometric Stereo
Authors: Thoma Papadhimitri, Paolo Favaro
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We investigate the problem of reconstructing normals, albedo and lights of Lambertian surfaces in uncalibrated photometric stereo under the perspective projection model. Our analysis is based on establishing the integrability constraint. In the orthographic projection case, it is well-known that when such constraint is imposed, a solution can be identified only up to 3 parameters, the so-called generalized bas-relief (GBR) ambiguity. We show that in the perspective projection case the solution is unique. We also propose a closed-form solution which is simple, efficient and robust. We test our algorithm on synthetic data and publicly available real data. Our quantitative tests show that our method outperforms all prior work of uncalibrated photometric stereo under orthographic projection.
A Joint Model for 2D and 3D Pose Estimation from a Single Image
Authors: Edgar Simo-Serra, Ariadna Quattoni, Carme Torras, Francesc Moreno-Noguer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate.
A Statistical Model for Recreational Trails in Aerial Images
Authors: Andrew Predoehl, Scott Morris, Kobus Barnard
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a statistical model of aerial images of recreational trails, and a method to infer trail routes in such images. We learn a set of textons describing the images, and use them to divide the image into super-pixels represented by their texton. We then learn, for each texton, the frequency of generating on-trail and off-trail pixels, and the direction of trail through on-trail pixels. From these, we derive an image likelihood function. We combine that with a prior model of trail length and smoothness, yielding a posterior distribution for trails, given an image. We search for good values of this posterior using a novel stochastic variation of Dijkstra's algorithm. Our experiments, on trail images and groundtruth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding method.
Learning Video Saliency from Human Gaze Using Candidate Selection
Authors: Dmitry Rudoy, Dan B. Goldman, Eli Shechtman, Lihi Zelnik-Manor
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: During recent years remarkable progress has been made in visual saliency modeling. Our interest is in video saliency. Since videos are fundamentally different from still images, they are viewed differently by human observers. For example, the time each video frame is observed is a fraction of a second, while a still image can be viewed leisurely. Therefore, video saliency estimation methods should differ substantially from image saliency methods. In this paper we propose a novel method for video saliency estimation, which is inspired by the way people watch videos. We explicitly model the continuity of the video by predicting the saliency map of a given frame, conditioned on the map from the previous frame. Furthermore, accuracy and computation speed are improved by restricting the salient locations to a carefully selected candidate set. We validate our method using two gaze-tracked video datasets and show we outperform the state-of-the-art.
Designing Category-Level Attributes for Discriminative Visual Recognition
Authors: Felix X. Yu, Liangliang Cao, Rogerio S. Feris, John R. Smith, Shih-Fu Chang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Attribute-based representation has shown great promises for visual recognition due to its intuitive interpretation and cross-category generalization property. However, human efforts are usually involved in the attribute designing process, making the representation costly to obtain. In this paper, we propose a novel formulation to automatically design discriminative "category-level attributes", which can be efficiently encoded by a compact category-attribute matrix. The formulation allows us to achieve intuitive and critical design criteria (category-separability, learnability) in a principled way. The designed attributes can be used for tasks of cross-category knowledge transfer, achieving superior performance over well-known attribute dataset Animals with Attributes (AwA) and a large-scale ILSVRC2010 dataset (1.2M images). This approach also leads to state-ofthe-art performance on the zero-shot learning task on AwA.
Dense Segmentation-Aware Descriptors
Authors: Eduard Trulls, Iasonas Kokkinos, Alberto Sanfeliu, Francesc Moreno-Noguer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this work we exploit segmentation to construct appearance descriptors that can robustly deal with occlusion and background changes. For this, we downplay measurements coming from areas that are unlikely to belong to the same region as the descriptor's center, as suggested by soft segmentation masks. Our treatment is applicable to any image point, i.e. dense, and its computational overhead is in the order of a few seconds. We integrate this idea with Dense SIFT, and also with Dense Scale and Rotation Invariant Descriptors (SID), delivering descriptors that are densely computable, invariant to scaling and rotation, and robust to background changes. We apply our approach to standard benchmarks on large displacement motion estimation using SIFT-flow and widebaseline stereo, systematically demonstrating that the introduction of segmentation yields clear improvements.
Modeling Mutual Visibility Relationship in Pedestrian Detection
Authors: Wanli Ouyang, Xingyu Zeng, Xiaogang Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Detecting pedestrians in cluttered scenes is a challenging problem in computer vision. The difficulty is added when several pedestrians overlap in images and occlude each other. We observe, however, that the occlusion/visibility statuses of overlapping pedestrians provide useful mutual relationship for visibility estimation the visibility estimation of one pedestrian facilitates the visibility estimation of another. In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians. The visibility relationship among pedestrians is learned from the deep model for recognizing co-existing pedestrians. Experimental results show that the mutual visibility deep model effectively improves the pedestrian detection results. Compared with existing image-based pedestrian detection approaches, our approach has the lowest average miss rate on the CaltechTrain dataset, the Caltech-Test dataset and the ETH dataset. Including mutual visibility leads to 4% 8% improvements on multiple benchmark datasets.
Discriminatively Trained And-Or Tree Models for Object Detection
Authors: Xi Song, Tianfu Wu, Yunde Jia, Song-Chun Zhu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper presents a method of learning reconfigurable And-Or Tree (AOT) models discriminatively from weakly annotated data for object detection. To explore the appearance and geometry space of latent structures effectively, we first quantize the image lattice using an overcomplete set of shape primitives, and then organize them into a directed acyclic And-Or Graph (AOG) by exploiting their compositional relations. We allow overlaps between child nodes when combining them into a parent node, which is equivalent to introducing an appearance Or-node implicitly for the overlapped portion. The learning of an AOT model consists of three components: (i) Unsupervised sub-category learning (i.e., branches of an object Or-node) with the latent structures in AOG being integrated out. (ii) Weaklysupervised part configuration learning (i.e., seeking the globally optimal parse trees in AOG for each sub-category). To search the globally optimal parse tree in AOG efficiently, we propose a dynamic programming (DP) algorithm. (iii) Joint appearance and structural parameters training under latent structural SVM framework. In experiments, our method is tested on PASCAL VOC 2007 and 2010 detection benchmarks of 20 object classes and outperforms comparable state-of-the-art methods.
Intrinsic Scene Properties from a Single RGB-D Image
Authors: Jonathan T. Barron, Jitendra Malik
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper we extend the "shape, illumination and reflectance from shading" (SIRFS) model [3, 4], which recovers intrinsic scene properties from a single image. Though SIRFS performs well on images of segmented objects, it performs poorly on images of natural scenes, which contain occlusion and spatially-varying illumination. We therefore present Scene-SIRFS, a generalization of SIRFS in which we have a mixture of shapes and a mixture of illuminations, and those mixture components are embedded in a "soft" segmentation of the input image. We additionally use the noisy depth maps provided by RGB-D sensors (in this case, the Kinect) to improve shape estimation. Our model takes as input a single RGB-D image and produces as output an improved depth map, a set of surface normals, a reflectance image, a shading image, and a spatially varying model of illumination. The output of our model can be used for graphics applications, or for any application involving RGB-D images.
Cross-View Image Geolocalization
Authors: Tsung-Yi Lin, Serge Belongie, James Hays
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: The recent availability of large amounts of geotagged imagery has inspired a number of data driven solutions to the image geolocalization problem. Existing approaches predict the location of a query image by matching it to a database of georeferenced photographs. While there are many geotagged images available on photo sharing and street view sites, most are clustered around landmarks and urban areas. The vast majority of the Earth's land area has no ground level reference photos available, which limits the applicability of all existing image geolocalization methods. On the other hand, there is no shortage of visual and geographic data that densely covers the Earth we examine overhead imagery and land cover survey data but the relationship between this data and ground level query photographs is complex. In this paper, we introduce a cross-view feature translation approach to greatly extend the reach of image geolocalization methods. We can often localize a query even if it has no corresponding groundlevel images in the database. A key idea is to learn the relationship between ground level appearance and overhead appearance and land cover attributes from sparsely available geotagged ground-level images. We perform experiments over a 1600 km d-region containing a variety of scenes and land cover types. For each query, our algorithm produces a probability density over the region of interest.
Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
Authors: Christian Scharfenberger, Alexander Wong, Khalil Fergani, John S. Zelek, David A. Clausi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: A novel statistical textural distinctiveness approach for robustly detecting salient regions in natural images is proposed. Rotational-invariant neighborhood-based textural representations are extracted and used to learn a set of representative texture atoms for defining a sparse texture model for the image. Based on the learnt sparse texture model, a weighted graphical model is constructed to characterize the statistical textural distinctiveness between all representative texture atom pairs. Finally, the saliency of each pixel in the image is computed based on the probability of occurrence of the representative texture atoms, their respective statistical textural distinctiveness based on the constructed graphical model, and general visual attentive constraints. Experimental results using a public natural image dataset and a variety of performance evaluation metrics show that the proposed approach provides interesting and promising results when compared to existing saliency detection methods.
Robust Multi-resolution Pedestrian Detection in Traffic Scenes
Authors: Junjie Yan, Xucong Zhang, Zhen Lei, Shengcai Liao, Stan Z. Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: The serious performance decline with decreasing resolution is the major bottleneck for current pedestrian detection techniques [14, 23]. In this paper, we take pedestrian detection in different resolutions as different but related problems, and propose a Multi-Task model to jointly consider their commonness and differences. The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background. For model learning, we present a coordinate descent procedure to learn the resolution aware transformations and deformable part model (DPM) based detector iteratively. In traffic scenes, there are many false positives located around vehicles, therefore, we further build a context model to suppress them according to the pedestrian-vehicle relationship. The context model can be learned automatically even when the vehicle annotations are not available. Our method reduces the mean miss rate to 60% for pedestrians taller than 30 pixels on the Caltech Pedestrian Benchmark, which noticeably outperforms previous state-of-the-art (71%).
Hypergraphs for Joint Multi-view Reconstruction and Multi-object Tracking
Authors: Martin Hofmann, Daniel Wolf, Gerhard Rigoll
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We generalize the network flow formulation for multiobject tracking to multi-camera setups. In the past, reconstruction of multi-camera data was done as a separate extension. In this work, we present a combined maximum a posteriori (MAP) formulation, which jointly models multicamera reconstruction as well as global temporal data association. A flow graph is constructed, which tracks objects in 3D world space. The multi-camera reconstruction can be efficiently incorporated as additional constraints on the flow graph without making the graph unnecessarily large. The final graph is efficiently solved using binary linear programming. On the PETS 2009 dataset we achieve results that significantly exceed the current state of the art.
Recognizing Activities via Bag of Words for Attribute Dynamics
Authors: Weixin Li, Qian Yu, Harpreet Sawhney, Nuno Vasconcelos
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this work, we propose a novel video representation for activity recognition that models video dynamics with attributes of activities. A video sequence is decomposed into short-term segments, which are characterized by the dynamics of their attributes. These segments are modeled by a dictionary of attribute dynamics templates, which are implemented by a recently introduced generative model, the binary dynamic system (BDS). We propose methods for learning a dictionary of BDSs from a training corpus, and for quantizing attribute sequences extracted from videos into these BDS codewords. This procedure produces a representation of the video as a histogram of BDS codewords, which is denoted the bag-of-words for attribute dynamics (BoWAD). An extensive experimental evaluation reveals that this representation outperforms other state-of-the-art approaches in temporal structure modeling for complex activity recognition.
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
Authors: Thomas Dean, Mark A. Ruzon, Mark Segal, Jonathon Shlens, Sudheendra Vijayanarasimhan, Jay Yagnik
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Many object detection systems are constrained by the time required to convolve a target image with a bank of filters that code for different aspects of an object's appearance, such as the presence of component parts. We exploit locality-sensitive hashing to replace the dot-product kernel operator in the convolution with a fixed number of hash-table probes that effectively sample all of the filter responses in time independent of the size of the filter bank. To show the effectiveness of the technique, we apply it to evaluate 100,000 deformable-part models requiring over a million (part) filters on multiple scales of a target image in less than 20 seconds using a single multi-core processor with 20GB of RAM. This represents a speed-up of approximately 20,000 times-four orders of magnitude-when compared with performing the convolutions explicitly on the same hardware. While mean average precision over the full set of 100,000 object classes is around 0.16 due in large part to the challenges in gathering training data and collecting ground truth for so many classes, we achieve a mAP of at least 0.20 on a third of the classes and 0.30 or better on about 20% of the classes.
Robust Object Co-detection
Authors: Xin Guo, Dong Liu, Brendan Jou, Mojun Zhu, Anni Cai, Shih-Fu Chang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Object co-detection aims at simultaneous detection of objects of the same category from a pool of related images by exploiting consistent visual patterns present in candidate objects in the images. The related image set may contain a mixture of annotated objects and candidate objects generated by automatic detectors. Co-detection differs from the conventional object detection paradigm in which detection over each test image is determined one-by-one independently without taking advantage of common patterns in the data pool. In this paper, we propose a novel, robust approach to dramatically enhance co-detection by extracting a shared low-rank representation of the object instances in multiple feature spaces. The idea is analogous to that of the well-known Robust PCA [28], but has not been explored in object co-detection so far. The representation is based on a linear reconstruction over the entire data set and the low-rank approach enables effective removal of noisy and outlier samples. The extracted low-rank representation can be used to detect the target objects by spectral clustering. Extensive experiments over diverse benchmark datasets demonstrate consistent and significant performance gains of the proposed method over the state-of-the-art object codetection method and the generic object detection methods without co-detection formulations.
Supervised Kernel Descriptors for Visual Recognition
Authors: Peng Wang, Jingdong Wang, Gang Zeng, Weiwei Xu, Hongbin Zha, Shipeng Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In visual recognition tasks, the design of low level image feature representation is fundamental. The advent of local patch features from pixel attributes such as SIFT and LBP, has precipitated dramatic progresses. Recently, a kernel view of these features, called kernel descriptors (KDES) [1], generalizes the feature design in an unsupervised fashion and yields impressive results. In this paper, we present a supervised framework to embed the image level label information into the design of patch level kernel descriptors, which we call supervised kernel descriptors (SKDES). Specifically, we adopt the broadly applied bag-of-words (BOW) image classification pipeline and a large margin criterion to learn the lowlevel patch representation, which makes the patch features much more compact and achieve better discriminative ability than KDES. With this method, we achieve competitive results over several public datasets comparing with stateof-the-art methods.
Measures and Meta-Measures for the Supervised Evaluation of Image Segmentation
Authors: Jordi Pont-Tuset, Ferran Marques
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper tackles the supervised evaluation of image segmentation algorithms. First, it surveys and structures the measures used to compare the segmentation results with a ground truth database; and proposes a new measure: the precision-recall for objects and parts. To compare the goodness of these measures, it defines three quantitative meta-measures involving six state of the art segmentation methods. The meta-measures consist in assuming some plausible hypotheses about the results and assessing how well each measure reflects these hypotheses. As a conclusion, this paper proposes the precision-recall curves for boundaries and for objects-and-parts as the tool of choice for the supervised evaluation of image segmentation. We make the datasets and code of all the measures publicly available.
A Fast Approximate AIB Algorithm for Distributional Word Clustering
Authors: Lei Wang, Jianjia Zhang, Luping Zhou, Wanqing Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Distributional word clustering merges the words having similar probability distributions to attain reliable parameter estimation, compact classification models and even better classification performance. Agglomerative Information Bottleneck (AIB) is one of the typical word clustering algorithms and has been applied to both traditional text classification and recent image recognition. Although enjoying theoretical elegance, AIB has one main issue on its computational efficiency, especially when clustering a large number of words. Different from existing solutions to this issue, we analyze the characteristics of its objective function -the loss of mutual information, and show that by merely using the ratio of word-class joint probabilities of each word, good candidate word pairs for merging can be easily identified. Based on this finding, we propose a fast approximate AIB algorithm and show that it can significantly improve the computational efficiency of AIB while well maintaining or even slightly increasing its classification performance. Experimental study on both text and image classification benchmark data sets shows that our algorithm can achieve more than 100 times speedup on large real data sets over the state-of-the-art method.
Separable Dictionary Learning
Authors: Simon Hawe, Matthias Seibert, Martin Kleinsteuber
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Many techniques in computer vision, machine learning, and statistics rely on the fact that a signal of interest admits a sparse representation over some dictionary. Dictionaries are either available analytically, or can be learned from a suitable training set. While analytic dictionaries permit to capture the global structure of a signal and allow a fast implementation, learned dictionaries often perform better in applications as they are more adapted to the considered class of signals. In imagery, unfortunately, the numerical burden for (i) learning a dictionary and for (ii) employing the dictionary for reconstruction tasks only allows to deal with relatively small image patches that only capture local image information. The approach presented in this paper aims at overcoming these drawbacks by allowing a separable structure on the dictionary throughout the learning process. On the one hand, this permits larger patch-sizes for the learning phase, on the other hand, the dictionary is applied efficiently in reconstruction tasks. The learning procedure is based on optimizing over a product of spheres which updates the dictionary as a whole, thus enforces basic dictionary properties such as mutual coherence explicitly during the learning procedure. In the special case where no separable structure is enforced, our method competes with state-of-the-art dictionary learning methods like K-SVD.