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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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2019 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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61 |
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3 |
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331-351 |
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Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning |
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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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Admin @ si @ DRR2019 |
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3172 |
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Author |
Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition |
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2020 |
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Neural Computing and Applications |
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NEUCOMA |
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32 |
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11579–11596 |
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Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the stochastic graphlet embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low-to-high-order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods. |
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DAG; 600.140; 600.121; 600.141 |
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Admin @ si @ DRL2020 |
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3348 |
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Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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33-38 |
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graph embedding; hierarchical graph representation; graph clustering; stochastic graphlet embedding; graph classification |
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Document pattern classification methods using graphs have received a lot of attention because of its robust representation paradigm and rich theoretical background. However, the way of preserving and the process for delineating documents with graphs introduce noise in the rendition of underlying data, which creates instability in the graph representation. To deal with such unreliability in representation, in this paper, we propose Pyramidal Stochastic Graphlet Embedding (PSGE).
Given a graph representing a document pattern, our method first computes a graph pyramid by successively reducing the base graph. Once the graph pyramid is computed, we apply Stochastic Graphlet Embedding (SGE) for each level of the pyramid and combine their embedded representation to obtain a global delineation of the original graph. The consideration of pyramid of graphs rather than just a base graph extends the representational power of the graph embedding, which reduces the instability caused due to noise and distortion. When plugged with support
vector machine, our proposed PSGE has outperformed the state-of-the-art results in recognition of handwritten words as well as graphical symbols |
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ICDAR |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ DRL2017 |
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3054 |
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Author |
Sounak Dey; Pau Riba; Anjan Dutta; Josep Llados; Yi-Zhe Song |
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Title |
Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval |
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2019 |
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IEEE Conference on Computer Vision and Pattern Recognition |
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2179-2188 |
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In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future research. |
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Long beach; CA; USA; June 2019 |
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CVPR |
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DAG; 600.140; 600.121; 600.097 |
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Admin @ si @ DRD2019 |
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3462 |
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Author |
Anjan Dutta; Umapada Pal; Josep Llados |
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Title |
Compact Correlated Features for Writer Independent Signature Verification |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition |
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This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300. |
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Cancun; Mexico; December 2016 |
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ICPR |
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DAG; 600.097 |
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Admin @ si @ DPL2016 |
Serial |
2875 |
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Author |
Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |
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Title |
Evaluation of the Effect of Improper Segmentation on Word Spotting |
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Journal Article |
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2019 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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22 |
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361-374 |
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Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods. |
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DAG; 600.097; 600.084; 600.121; 600.140; 600.129 |
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Admin @ si @ DNL2019 |
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3455 |
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Author |
Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |
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Title |
Local Binary Pattern for Word Spotting in Handwritten Historical Document |
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Conference Article |
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Year |
2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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574-583 |
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Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data |
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Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm. |
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Merida; Mexico; December 2016 |
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S+SSPR |
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DAG; 600.097; 602.006; 603.053 |
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Admin @ si @ DNL2016 |
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2876 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri |
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Title |
Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction |
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Journal Article |
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2018 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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60 |
Issue |
4 |
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512-524 |
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This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model. |
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DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129 |
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Admin @ si @ DMH2018a |
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3062 |
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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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2013 |
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Pattern Recognition |
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PR |
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46 |
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3 |
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752-768 |
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Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing |
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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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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203; 601.152 |
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Admin @ si @ DLP2012 |
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2127 |
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Anjan Dutta; Josep Llados; Umapada Pal |
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Bag-of-GraphPaths Descriptors for Symbol Recognition and Spotting in Line Drawings |
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2011 |
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In proceedings of 9th IAPR Workshop on Graphic Recognition |
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Graphical symbol recognition and spotting recently have become an important research activity. In this work we present a descriptor for symbols, especially for line drawings. The descriptor is based on the graph representation of graphical objects. We construct graphs from the vectorized information of the binarized images, where the critical points detected by the vectorization algorithm are considered as nodes and the lines joining them are considered as edges. Graph paths between two nodes in a graph are the finite sequences of nodes following the order from the starting to the final node. The occurrences of different graph paths in a given graph is an important feature, as they capture the geometrical and structural attributes of a graph. So the graph representing a symbol can efficiently be represent by the occurrences of its different paths. Their occurrences in a symbol can be obtained in terms of a histogram counting the number of some fixed prototype paths, we call the histogram as the Bag-of-GraphPaths (BOGP). These BOGP histograms are used as a descriptor to measure the distance among the symbols in vector space. We use the descriptor for three applications, they are: (1) classification of the graphical symbols, (2) spotting of the architectural symbols on floorplans, (3) classification of the historical handwritten words. |
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Seoul, Korea |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-36823-3 |
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GREC |
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DAG |
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Admin @ si @ DLP2011c |
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1825 |
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