|
Records |
Links |
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
StacMR: Scene-Text Aware Cross-Modal Retrieval |
Type |
Conference Article |
|
Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
2219-2229 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Virtual; January 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
WACV |
|
|
Notes |
DAG; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MRG2021a |
Serial |
3492 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
Real-time Lexicon-free Scene Text Retrieval |
Type |
Journal Article |
|
Year |
2021 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
|
Volume |
110 |
Issue |
|
Pages |
107656 |
|
|
Keywords |
|
|
|
Abstract |
In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.121; 600.129; 601.338 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MTD2021 |
Serial |
3493 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features |
Type |
Conference Article |
|
Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. |
|
|
Address |
Aspen; Colorado; USA; March 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
WACV |
|
|
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MDB2020 |
Serial |
3334 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
Type |
Conference Article |
|
Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
4022-4032 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Virtual; January 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
WACV |
|
|
Notes |
DAG; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MDB2021 |
Serial |
3491 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Anguelos Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Sparse Radial Sampling LBP for Writer Identification |
Type |
Conference Article |
|
Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
716-720 |
|
|
Keywords |
|
|
|
Abstract |
In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features. |
|
|
Address |
Nancy; France; August 2015 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICDAR |
|
|
Notes |
DAG; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ NBL2015 |
Serial |
2692 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings |
Type |
Conference Article |
|
Year |
2018 |
Publication |
International Workshop on Reproducible Research in Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
11455 |
Issue |
|
Pages |
71-82 |
|
|
Keywords |
|
|
|
Abstract |
Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ NDC2018 |
Serial |
3178 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Anjan Dutta |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Symbol Spotting in Graphical Documents by Serialized Subgraph Matching |
Type |
Report |
|
Year |
2010 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
|
|
Volume |
159 |
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
Master's thesis |
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG |
Approved |
no |
|
|
Call Number |
Admin @ si @ Dut2010 |
Serial |
1351 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Anjan Dutta |
![find book details (via ISBN) isbn](http://refbase.cvc.uab.es/img/isbn.gif)
|
|
Title |
Inexact Subgraph Matching Applied to Symbol Spotting in Graphical Documents |
Type |
Book Whole |
|
Year |
2014 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
There is a resurgence in the use of structural approaches in the usual object recognition and retrieval problem. Graph theory, in particular, graph matching plays a relevant role in that. Specifically, the detection of an object (or a part of that) in an image in terms of structural features can be formulated as a subgraph matching. Subgraph matching is a challenging task. Specially due to the presence of outliers most of the graph matching algorithms do not perform well in subgraph matching scenario. Also exact subgraph isomorphism has proven to be an NP-complete problem. So naturally, in graph matching community, there are lot of efforts addressing the problem of subgraph matching within suboptimal bound. Most of them work with approximate algorithms that try to get an inexact solution in estimated way. In addition, usual recognition must cope with distortion. Inexact graph matching consists in finding the best isomorphism under a similarity measure. Theoretically this thesis proposes algorithms for solving subgraph matching in an approximate and inexact way.
We consider the symbol spotting problem on graphical documents or line drawings from application point of view. This is a well known problem in the graphics recognition community. It can be further applied for indexing and classification of documents based on their contents. The structural nature of this kind of documents easily motivates one for giving a graph based representation. So the symbol spotting problem on graphical documents can be considered as a subgraph matching problem. The main challenges in this application domain is the noise and distortions that might come during the usage, digitalization and raster to vector conversion of those documents. Apart from that computer vision nowadays is not any more confined within a limited number of images. So dealing a huge number of images with graph based method is a further challenge.
In this thesis, on one hand, we have worked on efficient and robust graph representation to cope with the noise and distortions coming from documents. On the other hand, we have worked on different graph based methods and framework to solve the subgraph matching problem in a better approximated way, which can also deal with considerable number of images. Firstly, we propose a symbol spotting method by hashing serialized subgraphs. Graph serialization allows to create factorized substructures such as graph paths, which can be organized in hash tables depending on the structural similarities of the serialized subgraphs. The involvement of hashing techniques helps to reduce the search space substantially and speeds up the spotting procedure. Secondly, we introduce contextual similarities based on the walk based propagation on tensor product graph. These contextual similarities involve higher order information and more reliable than pairwise similarities. We use these higher order similarities to formulate subgraph matching as a node and edge selection problem in the tensor product graph. Thirdly, we propose near convex grouping to form near convex region adjacency graph which eliminates the limitations of traditional region adjacency graph representation for graphic recognition. Fourthly, we propose a hierarchical graph representation by simplifying/correcting the structural errors to create a hierarchical graph of the base graph. Later these hierarchical graph structures are matched with some graph matching methods. Apart from that, in this thesis we have provided an overall experimental comparison of all the methods and some of the state-of-the-art methods. Furthermore, some dataset models have also been proposed. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
Ph.D. thesis |
|
|
Publisher |
Ediciones Graficas Rey |
Place of Publication |
|
Editor |
Josep Llados;Umapada Pal |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-84-940902-4-0 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ Dut2014 |
Serial |
2465 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Anjan Dutta; Hichem Sahbi |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Stochastic Graphlet Embedding |
Type |
Journal Article |
|
Year |
2018 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
|
|
Volume |
|
Issue |
|
Pages |
1-14 |
|
|
Keywords |
Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality |
|
|
Abstract |
Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider
these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When
combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 602.167; 602.168; 600.097; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DuS2018 |
Serial |
3225 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Anjan Dutta; Jaume Gibert; Josep Llados; Horst Bunke; Umapada Pal |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Combination of Product Graph and Random Walk Kernel for Symbol Spotting in Graphical Documents |
Type |
Conference Article |
|
Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1663-1666 |
|
|
Keywords |
|
|
|
Abstract |
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. |
|
|
Address |
Tsukuba, Japan |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPR |
|
|
Notes |
DAG |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGL2012 |
Serial |
2125 |
|
Permanent link to this record |