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Author |
Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados |
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Title |
Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework |
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Conference Article |
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2018 |
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14th Asian Conference on Computer Vision |
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In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset. |
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Perth; Australia; December 2018 |
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ACCV |
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DAG; 600.097; 600.121; 600.129 |
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Admin @ si @ DDG2018a |
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3151 |
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Author |
Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal |
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Title |
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch |
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Conference Article |
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2018 |
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24th International Conference on Pattern Recognition |
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916 - 921 |
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In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets. |
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Beijing; China; August 2018 |
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ICPR |
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DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129 |
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Admin @ si @ DDG2018b |
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3152 |
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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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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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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 |
Sounak Dey |
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Title |
Mapping between Images and Conceptual Spaces: Sketch-based Image Retrieval |
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2020 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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This thesis presents several contributions to the literature of sketch based image retrieval (SBIR). In SBIR the first challenge we face is how to map two different domains to common space for effective retrieval of images, while tackling the different levels of abstraction people use to express their notion of objects around while sketching. To this extent we first propose a cross-modal learning framework that maps both sketches and text into a joint embedding space invariant to depictive style, while preserving semantics. Then we have also investigated different query types possible to encompass people's dilema in sketching certain world objects. For this we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set.
Finally, we explore 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 recognises 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. We also in this dissertation pave the path to the future direction of research in this domain. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados;Umapada Pal |
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978-84-121011-8-8 |
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DAG; 600.121 |
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Admin @ si @ Dey20 |
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3480 |
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Author |
Anjan Dutta; Jaume Gibert; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
Combination of Product Graph and Random Walk Kernel for Symbol Spotting in Graphical Documents |
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Conference Article |
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2012 |
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21st International Conference on Pattern Recognition |
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1663-1666 |
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This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging. |
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Tsukuba, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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DAG |
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Admin @ si @ DGL2012 |
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2125 |
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Author |
Arka Ujjal Dey; Suman Ghosh; Ernest Valveny |
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Title |
Don't only Feel Read: Using Scene text to understand advertisements |
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2018 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks. |
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Salt Lake City; Utah; USA; June 2018 |
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CVPRW |
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DAG; 600.121; 600.129 |
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Admin @ si @ DGV2018 |
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3551 |
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Arka Ujal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit |
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Title |
Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding |
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2021 |
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Pattern Recognition Letters |
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PRL |
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149 |
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164-171 |
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Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. |
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DAG; 600.121 |
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Admin @ si @ DGV2021 |
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3364 |
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Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
Near Convex Region Adjacency Graph and Approximate Neighborhood String Matching for Symbol Spotting in Graphical Documents |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1078-1082 |
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This paper deals with a subgraph matching problem in Region Adjacency Graph (RAG) applied to symbol spotting in graphical documents. RAG is a very important, efficient and natural way of representing graphical information with a graph but this is limited to cases where the information is well defined with perfectly delineated regions. What if the information we are interested in is not confined within well defined regions? This paper addresses this particular problem and solves it by defining near convex grouping of oriented line segments which results in near convex regions. Pure convexity imposes hard constraints and can not handle all the cases efficiently. Hence to solve this problem we have defined a new type of convexity of regions, which allows convex regions to have concavity to some extend. We call this kind of regions Near Convex Regions (NCRs). These NCRs are then used to create the Near Convex Region Adjacency Graph (NCRAG) and with this representation we have formulated the problem of symbol spotting in graphical documents as a subgraph matching problem. For subgraph matching we have used the Approximate Edit Distance Algorithm (AEDA) on the neighborhood string, which starts working after finding a key node in the input or target graph and iteratively identifies similar nodes of the query graph in the neighborhood of the key node. The experiments are performed on artificial, real and distorted datasets. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.045; 600.056; 600.061; 601.152 |
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Admin @ si @ DLB2013a |
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2358 |
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Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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A Product graph based method for dual subgraph matching applied to symbol spotting |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Product graph has been shown to be an efficient way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. This paper focuses on the two major limitations of the previous version of product graph: (1) Spurious nodes and edges in the graph representation and (2) Inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual graph representation on the original graph representing the graphical information and the product graph is computed between the dual graphs of the query graphs and the input graph.
The dual graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates similar path information of two graphs and exponentiating the adjacency matrix finds similar paths of greater lengths. Nodes joining similar paths between two graphs are found by combining different exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG |
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Admin @ si @ DLB2013b |
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2359 |
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