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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Alfons Juan-Ciscar; Gemma Sanchez |
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Title |
PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008 |
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2008 |
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Barcelona (Spain) |
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DAG @ dag @ JuS2008 |
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1054 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Albert Suso; Pau Riba; Oriol Ramos Terrades; Josep Llados |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
A Self-supervised Inverse Graphics Approach for Sketch Parametrization |
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Conference Article |
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Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
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12916 |
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28-42 |
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The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets. |
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Lausanne; Suissa; September 2021 |
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DAG; 600.121 |
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Admin @ si @ SRR2021 |
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3675 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Albert Gordo; Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Document Classification and Page Stream Segmentation for Digital Mailroom Applications |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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621-625 |
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In this paper we present a method for the segmentation of continuous page streams into multipage documents and the simultaneous classification of the resulting documents. We first present an approach to combine the multiple pages of a document into a single feature vector that represents the whole document. Despite its simplicity and low computational cost, the proposed representation yields results comparable to more complex methods in multipage document classification tasks. We then exploit this representation in the context of page stream segmentation. The most plausible segmentation of a page stream into a sequence of multipage documents is obtained by optimizing a statistical model that represents the probability of each segmented multipage document belonging to a particular class. Experimental results are reported on a large sample of real administrative multipage documents. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 600.056; 602.101 |
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Admin @ si @ GRK2013c |
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2345 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Albert Gordo; Jose Antonio Rodriguez; Florent Perronnin; Ernest Valveny |
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Title |
Leveraging category-level labels for instance-level image retrieval |
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Conference Article |
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2012 |
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25th IEEE Conference on Computer Vision and Pattern Recognition |
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3045-3052 |
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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. |
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Providence, Rhode Island |
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IEEE Xplore |
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1063-6919 |
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978-1-4673-1226-4 |
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CVPR |
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DAG |
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no |
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Admin @ si @ GRP2012 |
Serial |
2050 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Albert Gordo; Jaume Gibert; Ernest Valveny; Marçal Rusiñol |
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Title |
A Kernel-based Approach to Document Retrieval |
Type |
Conference Article |
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Year |
2010 |
Publication |
9th IAPR International Workshop on Document Analysis Systems |
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377–384 |
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In this paper we tackle the problem of document image retrieval by combining a similarity measure between documents and the probability that a given document belongs to a certain class. The membership probability to a specific class is computed using Support Vector Machines in conjunction with similarity measure based kernel applied to structural document representations. In the presented experiments, we use different document representations, both visual and structural, and we apply them to a database of historical documents. We show how our method based on similarity kernels outperforms the usual distance-based retrieval. |
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Boston; USA; |
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978-1-60558-773-8 |
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DAG |
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no |
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DAG @ dag @ GGV2010 |
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1431 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik |
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Title |
Asymmetric Distances for Binary Embeddings |
Type |
Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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36 |
Issue |
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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Notes |
DAG; 600.045; 605.203; 600.077 |
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no |
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Admin @ si @ GPG2014 |
Serial |
2272 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title |
Large-scale document image retrieval and classification with runlength histograms and binary embeddings |
Type |
Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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46 |
Issue |
7 |
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1898-1905 |
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visual document descriptor; compression; large-scale; retrieval; classification |
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We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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no |
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Admin @ si @ GPV2013 |
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2306 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title |
Document classification using multiple views |
Type |
Conference Article |
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Year |
2012 |
Publication |
10th IAPR International Workshop on Document Analysis Systems |
Abbreviated Journal |
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Pages |
33-37 |
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The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task. |
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Australia |
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IEEE Computer Society Washington |
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978-0-7695-4661-2 |
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DAS |
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DAG |
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no |
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Admin @ si @ GPV2012 |
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2049 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Albert Gordo; Florent Perronnin |
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Title |
A Bag-of-Pages Approach to Unordered Multi-Page Document Classification |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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Pages |
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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Address |
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 |
Approved |
no |
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Call Number |
Admin @ si @ GoP2010 |
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1480 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Albert Gordo; Florent Perronnin |
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Title |
Asymmetric Distances for Binary Embeddings |
Type |
Conference Article |
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Year |
2011 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
729 - 736 |
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Abstract |
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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no |
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Admin @ si @ GoP2011; IAM @ iam @ GoP2011 |
Serial |
1817 |
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