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Displaying 1-19 out of 19 total
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Yunchao Gong,Svetlana Lazebnik,Albert Gordo,Florent Perronnin
Issue Date:December 2013
pp. 2916-2929
This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantizat...
 
Scene recognition and weakly supervised object localization with deformable part-based models
Found in: Computer Vision, IEEE International Conference on
By Megha Pandey,Svetlana Lazebnik
Issue Date:November 2011
pp. 1307-1314
Weakly supervised discovery of common visual structure in highly variable, cluttered images is a key problem in recognition. We address this problem using deformable part-based models (DPM's) with latent SVM training [6]. These models have been introduced ...
 
Understanding scenes on many levels
Found in: Computer Vision, IEEE International Conference on
By Joseph Tighe,Svetlana Lazebnik
Issue Date:November 2011
pp. 335-342
This paper presents a framework for image parsing with multiple label sets. For example, we may want to simultaneously label every image region according to its basic-level object category (car, building, road, tree, etc.), superordinate category (animal, ...
 
Computing iconic summaries of general visual concepts
Found in: Computer Vision and Pattern Recognition Workshop
By Rahul Raguram, Svetlana Lazebnik
Issue Date:June 2008
pp. 1-8
This paper considers the problem of selecting iconic images to summarize general visual categories. We define iconic images as high-quality representatives of a large group of images consistent both in appearance and semantics. To find such groups, we perf...
 
Supervised Learning of Quantizer Codebooks by Information Loss Minimization
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Svetlana Lazebnik, Maxim Raginsky
Issue Date:July 2009
pp. 1294-1309
This paper proposes a technique for jointly quantizing continuous features and the posterior distributions of their class labels based on minimizing empirical information loss such that the quantizer index of a given feature vector approximates a sufficien...
 
Segmenting, Modeling, and Matching Video Clips Containing Multiple Moving Objects
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Fred Rothganger, Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Issue Date:March 2007
pp. 477-491
This paper presents a novel representation for dynamic scenes composed of multiple rigid objects that may undergo different motions and are observed by a moving camera. Multiview constraints associated with groups of affine-covariant scene patches and a no...
 
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Issue Date:June 2006
pp. 2169-2178
This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside ea...
 
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study
Found in: Computer Vision and Pattern Recognition Workshop
By Jianguo Zhang, Marcin Marszalek, Svetlana Lazebnik, Cordelia Schmid
Issue Date:June 2006
pp. 13
Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracte...
 
A Maximum Entropy Framework for Part-Based Texture and Object Recognition
Found in: Computer Vision, IEEE International Conference on
By Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Issue Date:October 2005
pp. 832-838
This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of scale- or affine-invariant keypoints. Object classes are represented using a ...
 
A Sparse Texture Representation Using Local Affine Regions
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Issue Date:August 2005
pp. 1265-1278
This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine Harris...
 
Segmenting, Modeling, and Matching Video Clips Containing Multiple Moving Objects
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Fred Rothganger, Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Issue Date:July 2004
pp. 914-921
This paper presents a novel representation for dynamic scenes composed of multiple rigid objects that may undergo different motions and be observed by a moving camera. Multi-view constraints associated with groups of affine-invariant scene patches and a no...
 
Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition
Found in: Computer Vision, IEEE International Conference on
By Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Issue Date:October 2003
pp. 649
This paper presents a framework for texture recognition based on local affine-invariant descriptors and their spatial layout. At modeling time, a generative model of local descriptors is learned from sample images using the EM algorithm. The EM framework a...
 
The Local Projective Shape of Smooth Surfaces and their Outlines
Found in: Computer Vision, IEEE International Conference on
By Svetlana Lazebnik, Jean Ponce
Issue Date:October 2003
pp. 83
This paper examines projectively invariant local properties of smooth curves and surfaces. Oriented projective differential geometry is proposed as a theoretical framework for establishing such invariants and describing the local shape of surfaces and thei...
 
A Sparse Texture Representation Using Affine-Invariant Regions
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Issue Date:June 2003
pp. 319
This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and non-rigid deformations. At the feature extraction stage, a sparse set of affine-invar...
 
3D Object Modeling and Recognition Using Affine-Invariant Patches and Multi-View Spatial Constraints
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Fredrick Rothganger, Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Issue Date:June 2003
pp. 272
This paper presents a novel representation for three-dimensional objects in terms of affine-invariant image patches and their spatial relationships. Multi-view constraints associated with groups of patches are combined with a normalized representation of t...
 
On Computing Exact Visual Hulls of Solids Bounded by Smooth Surfaces
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Svetlana Lazebnik, Edmond Boyer, Jean Ponce
Issue Date:December 2001
pp. 156
This paper presents a method for computing the visual hull that is based on two novel representations: the rim mesh, which describes the connectivity of contour generators on the object surface; and the visual hull mesh, which describes the exact structure...
 
Asymmetric Distances for Binary Embeddings
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Albert Gordo,Florent Perronnin, Yunchao Gong,Svetlana Lazebnik
Issue Date:January 2014
pp. 33-47
In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is...
 
Finding Things: Image Parsing with Regions and Per-Exemplar Detectors
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Joseph Tighe,Svetlana Lazebnik
Issue Date:June 2013
pp. 3001-3008
This paper presents a system for image parsing, or labeling each pixel in an image with its semantic category, aimed at achieving broad coverage across hundreds of object categories, many of them sparsely sampled. The system combines region-level features ...
 
Learning Binary Codes for High-Dimensional Data Using Bilinear Projections
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Yunchao Gong,Sanjiv Kumar,Henry A. Rowley,Svetlana Lazebnik
Issue Date:June 2013
pp. 484-491
Recent advances in visual recognition indicate that to achieve good retrieval and classification accuracy on large-scale datasets like Image Net, extremely high-dimensional visual descriptors, e.g., Fisher Vectors, are needed. We present a novel method for...
 
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