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Author |
Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik |
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Title |
Asymmetric Distances for Binary Embeddings |
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Journal Article |
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Year |
2014 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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36 |
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1 |
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33-47 |
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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), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). 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. |
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0162-8828 |
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DAG; 600.045; 605.203; 600.077 |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ GPG2014 |
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2272 |
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Author |
Albert Gordo |
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Title |
Document Image Representation, Classification and Retrieval in Large-Scale Domains |
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2013 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Despite the “paperless office” ideal that started in the decade of the seventies, businesses still strive against an increasing amount of paper documentation. Companies still receive huge amounts of paper documentation that need to be analyzed and processed, mostly in a manual way. A solution for this task consists in, first, automatically scanning the incoming documents. Then, document images can be analyzed and information can be extracted from the data. Documents can also be automatically dispatched to the appropriate workflows, used to retrieve similar documents in the dataset to transfer information, etc.
Due to the nature of this “digital mailroom”, we need document representation methods to be general, i.e., able to cope with very different types of documents. We need the methods to be sound, i.e., able to cope with unexpected types of documents, noise, etc. And, we need to methods to be scalable, i.e., able to cope with thousands or millions of documents that need to be processed, stored, and consulted. Unfortunately, current techniques of document representation, classification and retrieval are not apt for this digital mailroom framework, since they do not fulfill some or all of these requirements.
Through this thesis we focus on the problem of document representation aimed at classification and retrieval tasks under this digital mailroom framework. We first propose a novel document representation based on runlength histograms, and extend it to cope with more complex documents such as multiple-page documents, or documents that contain more sources of information such as extracted OCR text. Then we focus on the scalability requirements and propose a novel binarization method which we dubbed PCAE, as well as two general asymmetric distances between binary embeddings that can significantly improve the retrieval results at a minimal extra computational cost. Finally, we note the importance of supervised learning when performing large-scale retrieval, and study several approaches that can significantly boost the results at no extra cost at query time. |
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Barcelona |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Ernest Valveny;Florent Perronnin |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ Gor2013 |
Serial |
2277 |
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Author |
Albert Gordo |
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Title |
A Cyclic Page Layout Descriptor for Document Classification & Retrieval |
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Report |
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2009 |
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CVC Technical Report |
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128 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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CIC;DAG |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ Gor2009 |
Serial |
2387 |
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Author |
Albert Gordo; Florent Perronnin |
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Title |
Asymmetric Distances for Binary Embeddings |
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Conference Article |
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2011 |
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IEEE Conference on Computer Vision and Pattern Recognition |
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729 - 736 |
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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. |
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Providence, RI |
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978-1-4577-0394-2 |
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CVPR |
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DAG |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ GoP2011; IAM @ iam @ GoP2011 |
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1817 |
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Author |
Albert Gordo; Florent Perronnin |
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Title |
A Bag-of-Pages Approach to Unordered Multi-Page Document Classification |
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Conference Article |
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2010 |
Publication |
20th International Conference on Pattern Recognition |
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1920–1923 |
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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. |
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Istanbul (Turkey) |
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1051-4651 |
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978-1-4244-7542-1 |
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ICPR |
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DAG |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ GoP2010 |
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1480 |
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Author |
Lluis Gomez |
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Title |
Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding |
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2016 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes.
For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any
language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision.
Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which
text emerges as a perceptually significant group of atomic objects.
In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods.
Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve
the overall tracking performance of the system.
Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key
characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme. |
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Ph.D. thesis |
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Dimosthenis Karatzas |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ Gom2016 |
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2891 |
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Author |
Lluis Gomez |
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Title |
Perceptual Organization for Text Extraction in Natural Scenes |
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Report |
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2012 |
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CVC Technical Report |
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173 |
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Bellaterra |
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Master's thesis |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ Gom2012 |
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2309 |
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Author |
Raul Gomez |
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Title |
Exploiting the Interplay between Visual and Textual Data for Scene Interpretation |
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2020 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Machine learning experimentation under controlled scenarios and standard datasets is necessary to compare algorithms performance by evaluating all of them in the same setup. However, experimentation on how those algorithms perform on unconstrained data and applied tasks to solve real world problems is also a must to ascertain how that research can contribute to our society.
In this dissertation we experiment with the latest computer vision and natural language processing algorithms applying them to multimodal scene interpretation. Particularly, we research on how image and text understanding can be jointly exploited to address real world problems, focusing on learning from Social Media data.
We address several tasks that involve image and textual information, discuss their characteristics and offer our experimentation conclusions. First, we work on detection of scene text in images. Then, we work with Social Media posts, exploiting the captions associated to images as supervision to learn visual features, which we apply to multimodal semantic image retrieval. Subsequently, we work with geolocated Social Media images with associated tags, experimenting on how to use the tags as supervision, on location sensitive image retrieval and on exploiting location information for image tagging. Finally, we work on a specific classification problem of Social Media publications consisting on an image and a text: Multimodal hate speech classification. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Dimosthenis Karatzas;Lluis Gomez;Jaume Gibert |
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978-84-121011-7-1 |
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DAG; 600.121 |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ Gom20 |
Serial |
3479 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild |
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Journal Article |
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2017 |
Publication |
Pattern Recognition |
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PR |
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70 |
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60-74 |
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Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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DAG; 600.084; 601.197; 600.121; 600.129 |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ GoK2017 |
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2886 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
A fine-grained approach to scene text script identification |
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Conference Article |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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192-197 |
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This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online. |
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Santorini; Grecia; April 2016 |
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DAS |
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DAG; 601.197; 600.084 |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Admin @ si @ GoK2016b |
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2863 |
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