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Author |
Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
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Title |
Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
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2017 |
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Graphics Recognition. Current Trends and Challenges |
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9657 |
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Springer |
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B. Lamiroy; R Dueire Lins |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JLR2017 |
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2928 |
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Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
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Title |
Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs |
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Conference Article |
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2017 |
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8th Iberian Conference on Pattern Recognition and Image Analysis |
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Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines |
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We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction. |
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Faro; Portugal; June 2017 |
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IbPRIA |
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DAG; 600.097; 600.121 |
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Admin @ si @ JRL2017a |
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2953 |
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Author |
Lasse Martensson; Anders Hast; Alicia Fornes |
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Title |
Word Spotting as a Tool for Scribal Attribution |
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Conference Article |
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2017 |
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2nd Conference of the association of Digital Humanities in the Nordic Countries |
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87-89 |
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Gothenburg; Suecia; March 2017 |
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978-91-88348-83-8 |
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DHN |
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DAG; 600.097; 600.121 |
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Admin @ si @ MHF2017 |
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2954 |
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Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
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Title |
Learning structural loss parameters on graph embedding applied on symbolic graphs |
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Conference Article |
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Year |
2017 |
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12th IAPR International Workshop on Graphics Recognition |
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We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. |
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Kyoto; Japan; November 2017 |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JRL2017b |
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3073 |
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Author |
Sounak Dey; Anjan Dutta; Josep Llados; Alicia Fornes; Umapada Pal |
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Title |
Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario |
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Conference Article |
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2017 |
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12th IAPR International Workshop on Graphics Recognition |
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31-32 |
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One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ DDL2017 |
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3057 |
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Author |
Alicia Fornes; Beata Megyesi; Joan Mas |
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Title |
Transcription of Encoded Manuscripts with Image Processing Techniques |
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Conference Article |
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Year |
2017 |
Publication |
Digital Humanities Conference |
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441-443 |
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DH |
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DAG; 600.097; 600.121 |
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Admin @ si @ FMM2017 |
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3061 |
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Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal |
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Title |
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification |
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Miscellaneous |
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Year |
2018 |
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Arxiv |
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Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction. |
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DAG; 600.097; 600.121 |
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Admin @ si @ DDT2018 |
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3085 |
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Author |
Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
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Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
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Journal Article |
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2018 |
Publication |
Pattern Recognition |
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PR |
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80 |
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64-82 |
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Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
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Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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DAG; 600.097; 600.121 |
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Admin @ si @ RSJ2018 |
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3096 |
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Author |
Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam |
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Title |
Subgraph spotting in graph representations of comic book images |
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Journal Article |
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2018 |
Publication |
Pattern Recognition Letters |
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PRL |
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112 |
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118-124 |
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Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images |
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Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. |
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DAG; 600.097; 600.121 |
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Admin @ si @ LLD2018 |
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3150 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
A probabilistic framework for handwritten text line segmentation |
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Miscellaneous |
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2018 |
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Arxiv |
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Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning |
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We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step. |
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DAG; 600.097; 600.121 |
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Admin @ si @ CrR2018 |
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3253 |
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