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Author |
Marçal Rusiñol; Josep Llados; Gemma Sanchez |
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Title |
Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings |
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Journal Article |
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Year |
2010 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
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Volume |
13 |
Issue |
3 |
Pages |
321-331 |
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Abstract |
In this paper, we address the problem of symbol spotting in technical document images applied to scanned and vectorized line drawings. Like any information spotting architecture, our approach has two components. First, symbols are decomposed in primitives which are compactly represented and second a primitive indexing structure aims to efficiently retrieve similar primitives. Primitives are encoded in terms of attributed strings representing closed regions. Similar strings are clustered in a lookup table so that the set median strings act as indexing keys. A voting scheme formulates hypothesis in certain locations of the line drawing image where there is a high presence of regions similar to the queried ones, and therefore, a high probability to find the queried graphical symbol. The proposed approach is illustrated in a framework consisting in spotting furniture symbols in architectural drawings. It has been proved to work even in the presence of noise and distortion introduced by the scanning and raster-to-vector processes. |
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Springer-Verlag |
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1433-7541 |
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DAG |
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DAG @ dag @ RLS2010 |
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1165 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate |
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Title |
Feature Extraction by Using Dual-Generalized Discriminative Common Vectors |
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Journal Article |
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Year |
2019 |
Publication |
Journal of Mathematical Imaging and Vision |
Abbreviated Journal |
JMIV |
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Volume |
61 |
Issue |
3 |
Pages |
331-351 |
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Keywords |
Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning |
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Abstract |
In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods. |
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DAG; ADAS; 600.084; 600.118; 600.121; 600.129 |
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no |
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Admin @ si @ DRR2019 |
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3172 |
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Author |
Albert Gordo; Jaume Gibert; Ernest Valveny; Marçal Rusiñol |
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Title |
A Kernel-based Approach to Document Retrieval |
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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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DAS |
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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 |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
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Title |
Learning to Learn from Web Data through Deep Semantic Embeddings |
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Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
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11134 |
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514-529 |
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In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 601.338; 600.121 |
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no |
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Admin @ si @ GGG2018a |
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3175 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa; I. Bardaji; Horst Bunke |
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Title |
Graph-based k-means clustering: A comparison of the set versus the generalized median graph |
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Conference Article |
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Year |
2009 |
Publication |
13th International Conference on Computer Analysis of Images and Patterns |
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Volume |
5702 |
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Pages |
342–350 |
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In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph. |
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Münster, Germany |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-03766-5 |
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CAIP |
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DAG |
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DAG @ dag @ FVS2009d |
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1219 |
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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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Notes |
DAG; 600.045; 600.056; 600.061; 602.006; 600.077 |
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no |
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Admin @ si @ AGF2014b |
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2485 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Efficient Exemplar Word Spotting |
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Conference Article |
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Year |
2012 |
Publication |
23rd British Machine Vision Conference |
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67.1- 67.11 |
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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. 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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1-901725-46-4 |
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BMVC |
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DAG |
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no |
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DAG @ dag @ AGF2012 |
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1984 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Sparse representation over learned dictionary for symbol recognition |
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Journal Article |
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Year |
2016 |
Publication |
Signal Processing |
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SP |
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125 |
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36-47 |
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Keywords |
Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
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In this paper we propose an original sparse vector model for symbol retrieval task. More specically, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. |
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DAG; 600.061; 600.077 |
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no |
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Admin @ si @ DTR2016 |
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2946 |
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Author |
Anjan Dutta; Josep Llados; Umapada Pal |
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Title |
A Bag-of-Paths Based Serialized Subgraph Matching for Symbol Spotting in Line Drawings |
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Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
6669 |
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Pages |
620-627 |
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In this paper we propose an error tolerant subgraph matching algorithm based on bag-of-paths for solving the problem of symbol spotting in line drawings. Bag-of-paths is a factorized representation of graphs where the factorization is done by considering all the acyclic paths between each pair of connected nodes. Similar paths within the whole collection of documents are clustered and organized in a lookup table for efficient indexing. The lookup table contains the index key of each cluster and the corresponding list of locations as a single entry. The mean path of each of the clusters serves as the index key for each table entry. The spotting method is then formulated by a spatial voting scheme to the list of locations of the paths that are decided in terms of search of similar paths that compose the query symbol. Efficient indexing of common substructures helps to reduce the computational burden of usual graph based methods. The proposed method can also be seen as a way to serialize graphs which allows to reduce the complexity of the subgraph isomorphism. We have encoded the paths in terms of both attributed strings and turning functions, and presented a comparative results between them within the symbol spotting framework. Experimentations for matching different shape silhouettes are also reported and the method has been proved to work in noisy environment also. |
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Las Palmas de Gran Canaria. Spain |
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Springer Berlin Heidelberg |
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Berlin |
Editor |
Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
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LNCS |
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0302-9743 |
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978-3-642-21256-7 |
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IbPRIA |
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DAG |
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no |
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Call Number |
Admin @ si @ DLP2011a |
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1738 |
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Author |
Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados |
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Title |
Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval |
Type |
Conference Article |
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Year |
2011 |
Publication |
33rd European Conference on Information Retrieval |
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6611 |
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314-325 |
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In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset. |
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Dublin, Ireland |
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Springer |
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Berlin |
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P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch |
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LNCS |
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978-3-642-20160-8 |
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ECIR |
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DAG; RV;ADAS |
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Call Number |
Admin @ si @ RAK2011 |
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1737 |
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