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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...

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IEEE-Computer-Society Conference on Computer Vision and pattern Recognition Workshops

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

ISBN: (纸本)9781424439928

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 Csiszar), 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

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Assessing the aesthetic quality of photographs using generic image descriptors

Assessing the aesthetic quality of photographs using generic...

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IEEE International Conference on Computer Vision (ICCV)

作者： Marchesotti, Luca Perronnin, Florent Larlus, Diane Csurka, Gabriela Textual and Visual Pattern Analysis (TVPA) Xerox Research Centre Europe (XRCE) France

ISBN: (纸本)9781457711022

In this paper, we automatically assess the aesthetic properties of images. In the past, this problem has been addressed by hand-crafting features which would correlate with best photographic practices (e.g. "Does this image respect the rule of thirds?") or with photographic techniques (e.g. "Is this image a macro?"). We depart from this line of research and propose to use generic image descriptors to assess aesthetic quality. We experimentally show that the descriptors we use, which aggregate statistics computed from low-level local features, implicitly encode the aesthetic properties explicitly used by state-of-the-art methods and outperform them by a significant margin.

关键词： Accuracy Feature extraction Histograms Image color analysis Layout Semantics visualization

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Modeling Images as Mixtures of Reference Images

Modeling Images as Mixtures of Reference Images

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IEEE-Computer-Society Conference on Computer Vision and pattern Recognition Workshops

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

ISBN: (纸本)9781424439928

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

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A framework for visual saliency detection with applications to image thumbnailing

A framework for visual saliency detection with applications ...

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12th IEEE International Conference on Computer Vision

作者： Marchesotti, Luca Cifarelli, Claudio Csurka, Gabriela Textual and Visual Pattern Analysis (TVPA) Xerox Research Centre Europe (XRCE) France

ISBN: (纸本)9781424444199

We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image;secondly, we build a simple classifier and we use it to generate saliency maps. Finally, we refine the maps and we extract thumbnails. We show that in spite of its simplicity, our framework outperforms state-of-the-art approaches. Another advantage is its ability to deal with visual pop-up and application/task-driven saliency, if appropriately annotated images are available.

关键词： visualization

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Leveraging Category-Level Labels For Instance-Level Image Retrieval

Leveraging Category-Level Labels For Instance-Level Image Re...

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IEEE Conference on Computer Vision and pattern Recognition (CVPR)

作者： Gordo, Albert Rodriguez-Serrano, Jose A. Perronnin, Florent Valveny, Ernest Textual and Visual Pattern Analysis Xerox Research Center Europe France

ISBN: (纸本)9781467312288

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 datasets: 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： Image retrieval

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Fisher kernels for handwritten word-spotting

Fisher kernels for handwritten word-spotting

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ICDAR2009 - 10th International Conference on Document analysis and Recognition

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

ISBN: (纸本)9780769537252

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 image. This 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. © 2009 IEEE.

关键词： pattern recognition

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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...

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International Conference on pattern Recognition

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

ISBN: (纸本)9780769541099

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. © 2010 IEEE.

关键词： Information retrieval systems

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Asymmetric distances for binary embeddings

Asymmetric distances for binary embeddings

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

ISBN: (纸本)9781457703942

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. © 2011 IEEE.

关键词： Hamming distance

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Handwritten word-image retrieval with synthesized typed queries

Handwritten word-image retrieval with synthesized typed quer...

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ICDAR2009 - 10th International Conference on Document analysis and Recognition

作者： Rodríguez-Serrano, José A. Perronnin, Florent Computer Vision Centre Universitat Autonoma de Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France Computer Science Department Loughborough University United Kingdom

ISBN: (纸本)9780769537252

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 "on the fly" 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 "vocabulary" 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 SC-HMM is crucial. To the best of the authors' knowledge, this is the first work to address this issue in a non-trivial manner. © 2009 IEEE.

关键词： Image retrieval

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26th IEEE Conference on Computer Vision and pattern Recognition (CVPR 2008), vol.7

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

We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector set with a Gaussian mixture model (GMM) and to compute a probabilistic measure of similarity between the GMMs. The main contribution of this paper is to model each vector set with a GMM adapted from a common "universal" GMM using the maximum a posteriori (MAP) criterion. The advantages of this approach are twofold. MAP provides a more accurate estimate of the GMM parameters compared to standard maximum likelihood estimation (MLE) in the challenging case where the cardinality of the vector set is small. Moreover, there is a correspondence between the Gaussians of two GMMs adapted from a common distribution and one can take advantage of this fact to compute efficiently the probabilistic similarity. This work is applied to the image categorization problem: images are modeled as bags of low-level features and classification is performed using a kernel classifier based on the proposed similarity measure. Experimental results on the PASCAL VOC 2006 and VOC 2007 databases show the excellent performance of our approach.

关键词： Vocabulary Costs Maximum likelihood estimation Kernel pattern analysis Distributed computing Performance evaluation Histograms Feature extraction Sampling methods

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