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Modeling images as mixtures of reference images

Modeling images as mixtures of reference images

引用

Conference on Computer Vision and pattern Recognition (CVPR)

作者： Florent Perronnin Yan Liu Xerox Research Centre Europe (XRCE) Textual and Visual Pattern Analysis (TVPA) France

A state-of-the-art approach to measure the similarity of two images is to model each image by a continuous distribution, generally a Gaussian mixture model (GMM), and to compute a probabilistic similarity between the GMMs. One limitation of traditional measures such as the Kullback-Leibler (KL) divergence and the probability product kernel (PPK) is that they measure a global match of distributions. This paper introduces a novel image representation. We propose to approximate an image, modeled by a GMM, as a convex combination of K reference image GMMs, and then to describe the image as the K-dimensional vector of mixture weights. The computed weights encode a similarity that favors local matches (i.e. matches of individual Gaussians) and is therefore fundamentally different from the KL or PPK. Although the computation of the mixture weights is a convex optimization problem, its direct optimization is difficult. We propose two approximate optimization algorithms: the first one based on traditional sampling methods, the second one based on a variational bound approximation of the true objective function. We apply this novel representation to the image categorization problem and compare its performance to traditional kernel-based methods. We demonstrate on the PASCAL VOC 2007 dataset a consistent increase in classification accuracy.

关键词： Image representation Kernel Vocabulary pattern analysis europe Distributed computing Optimization methods Approximation algorithms Sampling methods Image classification

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High-Dimensional Signature Compression for Large-Scale Image Classification

High-Dimensional Signature Compression for Large-Scale Image...

引用

IEEE Conference on Computer Vision and pattern Recognition

作者： Jorge Sanchez Florent Perronnin Textual and Visual Pattern Analysis (TVPA) group Xerox Research Centre Europe (XRCE)

ISBN: (纸本)9781457703942

We address image classification on a large-scale, i.e. when a large number of images and classes are involved. First, we study classification accuracy as a function of the image signature dimensionality and the training set size. We show experimentally that the larger the training set, the higher the impact of the dimensionality on the accuracy. In other words, high-dimensional signatures are important to obtain state-of-the-art results on large datasets. Second, we tackle the problem of data compression on very large signatures (on the order of 10~5 dimensions) using two lossy compression strategies: a dimensionality reduction technique known as the hash kernel and an encoding technique based on product quantizers. We explain how the gain in storage can be traded against a loss in accuracy and/or an increase in CPU cost. We report results on two large databases - Image-Net and a dataset of 1M Flickr images - showing that we can reduce the storage of our signatures by a factor 64 to 128 with little loss in accuracy. Integrating the decompression in the classifier learning yields an efficient and scalable training algorithm. On ILSVRC2010 we report a 74.3% accuracy at top-5, which corresponds to a 2.5% absolute improvement with respect to the state-of-the-art. On a subset of 10K classes of ImageNet we report a top-1 accuracy of 16.7%, a relative improvement of 160% with respect to the state-of-the-art.

关键词： Image classification signatures Dataset images Arts Data compression central processing units dimensionality reduction

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Handwritten Word Image Retrieval with Synthesized Typed Queries

Handwritten Word Image Retrieval with Synthesized Typed Quer...

引用

International Conference on Document analysis and Recognition

作者： José A. Rodríguez-Serrano Florent Perronnin Computer Vision Centre Universitat Authnòma de Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France

We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained ldquoon the flyrdquo with images rendered from the searched words in one or multiple computer fonts. To reduce the mismatch between the typed-text prototypes and the candidate handwritten images, we make use of: (i) local gradient histogram(LGH) features, which were shown to model word shapes robustly, and (ii) semi-continuous hidden Markov models(SC-HMM), in which the typed-text models are constrained to a ldquovocabularyrdquo of handwritten shapes, thus learning a link between both types of data. Experiments show that the proposed method is effective in retrieving handwritten words, and the comparison to alternative methods reveals that the contribution of both the LGH features and the SCHMM is crucial. To the best of the authorspsila knowledge, this is the first work to address this issue in a non-trivial manner.

关键词： Image retrieval Hidden Markov models Prototypes Shape Robustness pattern analysis Handwriting recognition Rendering (computer graphics) Writing Text analysis

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Asymmetric distances for binary embeddings

Asymmetric distances for binary embeddings

引用

Conference on Computer Vision and pattern Recognition (CVPR)

作者： Albert Gordo Florent Perronnin Computer Vision Center Universitat Autònoma Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France

In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique - PCA Embedding (PCAE) - which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH.

关键词： Databases Kernel Euclidean distance Principal component analysis Approximation methods Table lookup Random access memory

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Fisher Kernels for Handwritten Word-spotting

Fisher Kernels for Handwritten Word-spotting

引用

International Conference on Document analysis and Recognition

作者： Florent Perronnin Jose A. Rodriguez-Serrano Textual and Visual Pattern Analysis Xerox Research Centre Europe France Computer Vision Centre Universitat Authnòma de Barcelona Spain

The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to apply this framework to handwritten word-spotting. Given a word image and a keyword generative model, the idea is to generate a vector which describes how the parameters of the keyword model should be modified to best fit the word *** vector can then be used as the input of a discriminative classifier. We compare the performance of the proposed approach with that of a generative baseline on a challenging real-world dataset of customer letters. When the kernel used by the classifier is linear, the performance improvement is marginal but the proposed system is approximately 15 times faster than the baseline. If we use a non-linear kernel devised for this task, we obtain a 15% relative reduction of the error but the detector is approximately 15 times slower.

关键词： Kernel Hidden Markov models pattern classification pattern analysis europe Text analysis Handwriting recognition pattern recognition Computer vision Vectors

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A Bag-of-Pages Approach to Unordered Multi-page Document Classification

A Bag-of-Pages Approach to Unordered Multi-page Document Cla...

引用

International Conference on pattern Recognition

作者： Albert Gordo Florent Perronnin Computer Vision Center Universitat Autònoma Barcelona Spain Xerox Research Centre Europe Textual and Visual Pattern Analysis (TVPA) France

ISBN: (纸本)9781424475421

We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider several refinements over this initial approach. We show on two challenging datasets that the proposed approach significantly outperforms a baseline system.

关键词： Histograms Training Accuracy Feature extraction visualization Kernel Hidden Markov models

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Unsupervised writer style adaptation for handwritten word spotting

Unsupervised writer style adaptation for handwritten word sp...

引用

International Conference on pattern Recognition

作者： Jose A. Rodriguez Florent Perronnin Gemma Sanchez Josep Llados Xerox Research Centre Europe Textual and Visual Pattern Analysis (TVPA) France Computer Vision Center (CVC) Universitat Authnòma de Barcelona Spain

ISBN: (纸本)9781424421749

We propose a novel approach for writer adaptation in a word spotting task. The method exploits the fact that a semi-continuous hidden Markov model separates the word model parameters into (i) a shared codebook of shapes and (ii) a set of word-specific parameters. Our main contribution is to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best knowledge of the authors, this is the first work dealing with adaptation for word spotting.

关键词： Hidden Markov models Character recognition Handwriting recognition Shape Testing pattern analysis europe Computer vision pattern classification Degradation

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Leveraging category-level labels for instance-level image retrieval

Leveraging category-level labels for instance-level image re...

引用

Conference on Computer Vision and pattern Recognition (CVPR)

作者： Albert Gordoa José A. Rodríguez-Serrano Florent Perronnin Ernest Valveny Computer Vision Center Universitat Autònoma de Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Center Europe France

In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor which is subsequently encoded into a small number of bits. We note that most encoding techniques include an unsupervised dimensionality reduction step. Our goal in this work is to learn a better subspace in a supervised manner. We especially raise the following question: "can category-level labels be used to learn such a subspace?" To answer this question, we experiment with four learning techniques: the first one is based on a metric learning framework, the second one on attribute representations, the third one on Canonical Correlation analysis (CCA) and the fourth one on Joint Subspace and Classifier Learning (JSCL). While the first three approaches have been applied in the past to the image retrieval problem, we believe we are the first to show the usefulness of JSCL in this context. In our experiments, we use ImageNet as a source of category-level labels and report retrieval results on two standard dataseis: INRIA Holidays and the University of Kentucky benchmark. Our experimental study shows that metric learning and attributes do not lead to any significant improvement in retrieval accuracy, as opposed to CCA and JSCL. As an example, we report on Holidays an increase in accuracy from 39.3% to 48.6% with 32-dimensional representations. Overall JSCL is shown to yield the best results.

关键词： Measurement Vectors Principal component analysis Accuracy Encoding Correlation Image retrieval

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A family of contextual measures of similarity between distributions with application to image retrieval

A family of contextual measures of similarity between distri...

引用

Conference on Computer Vision and pattern Recognition (CVPR)

作者： Florent Perronnin Yan Liu Jean-Michel Renders Textual and Visual Pattern Analysis (TVPA) Xerox Research Centre Europe France Laboratoire dInformatique en Image et Systmes dInformation (LIRIS) Ecole Centrale de Lyon Limited France

We introduce a novel family of contextual measures of similarity between distributions: the similarity between two distributions q and p is measured in the context of a third distribution u. In our framework any traditional measure of similarity / dissimilarity has its contextual counterpart. We show that for two important families of divergences (Bregman and Csisz'ar), the contextual similarity computation consists in solving a convex optimization problem. We focus on the case of multinomials and explain how to compute in practice the similarity for several well-known measures. These contextual measures are then applied to the image retrieval problem. In such a case, the context u is estimated from the neighbors of a query q. One of the main benefits of our approach lies in the fact that using different contexts, and especially contexts at multiple scales (i.e. broad and narrow contexts), provides different views on the same problem. Combining the different views can improve retrieval accuracy. We will show on two very different datasets (one of photographs, the other of document images) that the proposed measures have a relatively small positive impact on macro Average Precision (which measures purely ranking) and a large positive impact on micro Average Precision (which measures both ranking and consistency of the scores across multiple queries).

关键词： Image retrieval Q measurement Painting Information retrieval Cats Rendering (computer graphics) pattern analysis europe Humans Animals

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