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Author |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title |
Large-scale document image retrieval and classification with runlength histograms and binary embeddings |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
7 |
Pages |
1898-1905 |
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Keywords |
visual document descriptor; compression; large-scale; retrieval; classification |
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Abstract |
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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Call Number |
Admin @ si @ GPV2013 |
Serial |
2306 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
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Title |
Fuzzy Multilevel Graph Embedding |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
2 |
Pages |
551-565 |
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Keywords |
Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic |
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Abstract |
Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. |
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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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Call Number |
Admin @ si @ LRL2013a |
Serial |
2270 |
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Author |
Anjan Dutta; Josep Llados; Umapada Pal |
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Title |
A symbol spotting approach in graphical documents by hashing serialized graphs |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
3 |
Pages |
752-768 |
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Keywords |
Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing |
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Abstract |
In this paper we propose a symbol spotting technique in graphical documents. Graphs are used to represent the documents and a (sub)graph matching technique is used to detect the symbols in them. We propose a graph serialization to reduce the usual computational complexity of graph matching. Serialization of graphs is performed by computing acyclic graph paths between each pair of connected nodes. Graph paths are one-dimensional structures of graphs which are less expensive in terms of computation. At the same time they enable robust localization even in the presence of noise and distortion. Indexing in large graph databases involves a computational burden as well. We propose a graph factorization approach to tackle this problem. Factorization is intended to create a unified indexed structure over the database of graphical documents. Once graph paths are extracted, the entire database of graphical documents is indexed in hash tables by locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. We have performed detailed experiments with various datasets of line drawings and compared our method with the state-of-the-art works. The results demonstrate the effectiveness and efficiency of our technique. |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203; 601.152 |
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Call Number |
Admin @ si @ DLP2012 |
Serial |
2127 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Segmentation-free Word Spotting with Exemplar SVMs |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
12 |
Pages |
3967–3978 |
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Keywords |
Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression |
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Abstract |
In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. |
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DAG; 600.045; 600.056; 600.061; 602.006; 600.077 |
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Admin @ si @ AGF2014b |
Serial |
2485 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Boosting the Handwritten Word Spotting Experience by Including the User in the Loop |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
3 |
Pages |
1063–1072 |
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Keywords |
Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling |
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Abstract |
In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list. |
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0031-3203 |
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DAG; 600.045; 600.061; 600.077 |
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Call Number |
Admin @ si @ RuL2013 |
Serial |
2343 |
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