Publicacions CVC -- Query Results

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	Author	Josep Llados; Dimosthenis Karatzas; Joan Mas; Gemma Sanchez
	Title	A Generic Architecture for the Conversion of Document Collections into Semantically Annotated Digital Archives			Type	Journal
	Year	2008	Publication	Journal of Universal Computer Science	Abbreviated Journal
	Volume	14	Issue	18	Pages	2912–2935
	Keywords	Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition
	Abstract
	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			Approved	no
	Call Number	DAG @ dag @ LKM2008			Serial	1142
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	Author	Marçal Rusiñol; Josep Llados; Gemma Sanchez
	Title	Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings			Type	Journal Article
	Year	2010	Publication	Pattern Analysis and Applications	Abbreviated Journal	PAA
	Volume	13	Issue	3	Pages	321-331
	Keywords
	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.
	Address
	Corporate Author				Thesis
	Publisher	Springer-Verlag	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1433-7541	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ RLS2010			Serial	1165
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	Author	Marçal Rusiñol; Josep Llados
	Title	A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices			Type	Journal Article
	Year	2009	Publication	International Journal on Document Analysis and Recognition	Abbreviated Journal	IJDAR
	Volume	12	Issue	2	Pages	83-96
	Keywords	Performance evaluation; Symbol Spotting; Graphics Recognition
	Abstract	Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors.
	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	1433-2833	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ RuL2009a			Serial	1166
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	Author	Miquel Ferrer; Ernest Valveny; F. Serratosa
	Title	Median Graphs: A Genetic Approach based on New Theoretical Properties			Type	Journal Article
	Year	2009	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	42	Issue	9	Pages	2003–2012
	Keywords	Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition
	Abstract	Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.
	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			Approved	no
	Call Number	DAG @ dag @ FVS2009b			Serial	1167
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	Author	Marçal Rusiñol; Agnes Borras; Josep Llados
	Title	Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images			Type	Journal Article
	Year	2010	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	31	Issue	3	Pages	188–201
	Keywords	Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings
	Abstract	This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	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	DAG @ dag @ RBL2010			Serial	1177
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	Author	Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados
	Title	Blurred Shape Model for Binary and Grey-level Symbol Recognition			Type	Journal Article
	Year	2009	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	30	Issue	15	Pages	1424–1433
	Keywords
	Abstract	Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance.
	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	HuPBA; DAG; MILAB			Approved	no
	Call Number	BCNPCL @ bcnpcl @ EFP2009a			Serial	1180
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	Author	Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone
	Title	Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework			Type	Journal Article
	Year	2009	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	31	Issue	9	Pages	1630–1644
	Keywords
	Abstract	The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes.
	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	0162-8828	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ RVT2009			Serial	1220
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	Author	Anjan Dutta; Zeynep Akata
	Title	Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval			Type	Conference Article
	Year	2019	Publication	32nd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	5089-5098
	Keywords
	Abstract	Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets.
	Address	Long beach; California; USA; June 2019
	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	CVPR
	Notes	DAG; 600.141; 600.121			Approved	no
	Call Number	Admin @ si @ DuA2019			Serial	3268
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	Author	Marçal Rusiñol
	Title	Geometric and Structural-based Symbol Spotting. Application to Focused Retrieval in Graphic Document Collections			Type	Book Whole
	Year	2009	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Usually, pattern recognition systems consist of two main parts. On the one hand, the data acquisition and, on the other hand, the classification of this data on a certain category. In order to recognize which category a certain query element belongs to, a set of pattern models must be provided beforehand. An off-line learning stage is needed to train the classifier and to offer a robust classification of the patterns. Within the pattern recognition field, we are interested in the recognition of graphics and, in particular, on the analysis of documents rich in graphical information. In this context, one of the main concerns is to see if the proposed systems remain scalable with respect to the data volume so as it can handle growing amounts of symbol models. In order to avoid to work with a database of reference symbols, symbol spotting and on-the-fly symbol recognition methods have been introduced in the past years. Generally speaking, the symbol spotting problem can be defined as the identification of a set of regions of interest from a document image which are likely to contain an instance of a certain queriedn symbol without explicitly applying the whole pattern recognition scheme. Our application framework consists on indexing a collection of graphic-rich document images. This collection is queried by example with a single instance of the symbol to look for and, by means of symbol spotting methods we retrieve the regions of interest where the symbol is likely to appear within the documents. This kind of applications are known as focused retrieval methods. In order that the focused retrieval application can handle large collections of documents there is a need to provide an efficient access to the large volume of information that might be stored. We use indexing strategies in order to efficiently retrieve by similarity the locations where a certain part of the symbol appears. In that scenario, graphical patterns should be used as indices for accessing and navigating the collection of documents. These indexing mechanism allow the user to search for similar elements using graphical information rather than textual queries. Along this thesis we present a spotting architecture and different methods aiming to build a complete focused retrieval application dealing with a graphic-rich document collections. In addition, a protocol to evaluate the performance of symbol spotting systems in terms of recognition abilities, location accuracy and scalability is proposed.
	Address	Barcelona (Spain)
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Josep Llados
	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	DAG @ dag @ Rus2009			Serial	1264
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	Author	Agnes Borras
	Title	Contributions to the Content-Based Image Retrieval Using Pictorial Queries			Type	Book Whole
	Year	2009	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The broad access to digital cameras, personal computers and Internet, has lead to the generation of large volumes of data in digital form. If we want an effective usage of this huge amount of data, we need automatic tools to allow the retrieval of relevant information. Image data is a particular type of information that requires specific techniques of description and indexing. The computer vision field that studies these kind of techniques is called Content-Based Image Retrieval (CBIR). Instead of using text-based descriptions, a system of CBIR deals on properties that are inherent in the images themselves. Hence, the feature-based description provides a universal via of image expression in contrast with the more than 6000 languages spoken in the world. Nowadays, the CBIR is a dynamic focus of research that has derived in important applications for many professional groups. The potential fields of application can be such diverse as: the medical domain, the crime prevention, the protection of the intel- lectual property, the journalism, the graphic design, the web search, the preservation of cultural heritage, etc. The definition on the role of the user is a key point in the development of a CBIR application. The user is in charge to formulate the queries from which the images are retrieved. We have centered our attention on the image retrieval techniques that use queries based on pictorial information. We have identified a taxonomy composed by four main query paradigms: query-by-selection, query-by-iconic-composition, query- by-sketch and query-by-paint. Each one of these paradigms allows a different degree of user expressivity. From a simple image selection, to a complete painting of the query, the user takes control of the input in the CBIR system. Along the chapters of this thesis we have analyzed the influence that each query paradigm imposes in the internal operations of a CBIR system. Moreover, we have proposed a set of contributions that we have exemplified in the context of a final application.
	Address	Barcelona (Spain)
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication	Bellaterra	Editor	Josep Llados
	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	DAG @ dag @ Bor2009; IAM @ iam @ Bor2009			Serial	1269
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