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Author |
Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Title |
Determining the Best Suited Semantic Events for Cognitive Surveillance |
Type |
Journal Article |
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Year |
2011 |
Publication |
Expert Systems with Applications |
Abbreviated Journal |
EXSY |
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Volume |
38 |
Issue |
4 |
Pages |
4068–4079 |
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Keywords |
Cognitive surveillance; Event modeling; Content-based video retrieval; Ontologies; Advanced user interfaces |
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Abstract |
State-of-the-art systems on cognitive surveillance identify and describe complex events in selected domains, thus providing end-users with tools to easily access the contents of massive video footage. Nevertheless, as the complexity of events increases in semantics and the types of indoor/outdoor scenarios diversify, it becomes difficult to assess which events describe better the scene, and how to model them at a pixel level to fulfill natural language requests. We present an ontology-based methodology that guides the identification, step-by-step modeling, and generalization of the most relevant events to a specific domain. Our approach considers three steps: (1) end-users provide textual evidence from surveilled video sequences; (2) transcriptions are analyzed top-down to build the knowledge bases for event description; and (3) the obtained models are used to generalize event detection to different image sequences from the surveillance domain. This framework produces user-oriented knowledge that improves on existing advanced interfaces for video indexing and retrieval, by determining the best suited events for video understanding according to end-users. We have conducted experiments with outdoor and indoor scenes showing thefts, chases, and vandalism, demonstrating the feasibility and generalization of this proposal. |
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Elsevier |
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ISE |
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no |
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Admin @ si @ FBR2011a |
Serial |
1722 |
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Author |
Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Title |
Augmenting Video Surveillance Footage with Virtual Agents for Incremental Event Evaluation |
Type |
Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
32 |
Issue |
6 |
Pages |
878–889 |
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Abstract |
The fields of segmentation, tracking and behavior analysis demand for challenging video resources to test, in a scalable manner, complex scenarios like crowded environments or scenes with high semantics. Nevertheless, existing public databases cannot scale the presence of appearing agents, which would be useful to study long-term occlusions and crowds. Moreover, creating these resources is expensive and often too particularized to specific needs. We propose an augmented reality framework to increase the complexity of image sequences in terms of occlusions and crowds, in a scalable and controllable manner. Existing datasets can be increased with augmented sequences containing virtual agents. Such sequences are automatically annotated, thus facilitating evaluation in terms of segmentation, tracking, and behavior recognition. In order to easily specify the desired contents, we propose a natural language interface to convert input sentences into virtual agent behaviors. Experimental tests and validation in indoor, street, and soccer environments are provided to show the feasibility of the proposed approach in terms of robustness, scalability, and semantics. |
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Elsevier |
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ISE |
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no |
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Admin @ si @ FBR2011b |
Serial |
1723 |
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Author |
Albert Ali Salah; Theo Gevers; Nicu Sebe; Alessandro Vinciarelli |
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Title |
Computer Vision for Ambient Intelligence |
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Journal Article |
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Year |
2011 |
Publication |
Journal of Ambient Intelligence and Smart Environments |
Abbreviated Journal |
JAISE |
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Volume |
3 |
Issue |
3 |
Pages |
187-191 |
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ISE |
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no |
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Admin @ si @ SGS2011a |
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1725 |
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Author |
Farhan Riaz; Fernando Vilariño; Mario Dinis-Ribeiro; Miguel Coimbraln |
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Title |
Identifying Potentially Cancerous Tissues in Chromoendoscopy Images |
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Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
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Pages |
709-716 |
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Keywords |
Endoscopy, Computer Assisted Diagnosis, Gradient. |
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Abstract |
The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered. |
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Las Palmas de Gran Canaria. Spain |
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Springer |
Place of Publication |
Berlin |
Editor |
J. Vitria, J.M. Sanches, and M. Hernandez |
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LNCS |
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978-3-642-21256-7 |
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800 |
Expedition |
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Conference |
IbPRIA |
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Notes |
MV;SIAI |
Approved |
no |
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Call Number |
Admin @ si @ RVD2011; IAM @ iam @ RVD2011 |
Serial |
1726 |
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Author |
Koen E.A. van de Sande; Theo Gevers; Cees G.M. Snoek |
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Title |
Empowering Visual Categorization with the GPU |
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Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Multimedia |
Abbreviated Journal |
TMM |
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Volume |
13 |
Issue |
1 |
Pages |
60-70 |
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Abstract |
Visual categorization is important to manage large collections of digital images and video, where textual meta-data is often incomplete or simply unavailable. The bag-of-words model has become the most powerful method for visual categorization of images and video. Despite its high accuracy, a severe drawback of this model is its high computational cost. As the trend to increase computational power in newer CPU and GPU architectures is to increase their level of parallelism, exploiting this parallelism becomes an important direction to handle the computational cost of the bag-of-words approach. When optimizing a system based on the bag-of-words approach, the goal is to minimize the time it takes to process batches of images. Additionally, we also consider power usage as an evaluation metric. In this paper, we analyze the bag-of-words model for visual categorization in terms of computational cost and identify two major bottlenecks: the quantization step and the classification step. We address these two bottlenecks by proposing two efficient algorithms for quantization and classification by exploiting the GPU hardware and the CUDA parallel programming model. The algorithms are designed to (1) keep categorization accuracy intact, (2) decompose the problem and (3) give the same numerical results. In the experiments on large scale datasets it is shown that, by using a parallel implementation on the Geforce GTX260 GPU, classifying unseen images is 4.8 times faster than a quad-core CPU version on the Core i7 920, while giving the exact same numerical results. In addition, we show how the algorithms can be generalized to other applications, such as text retrieval and video retrieval. Moreover, when the obtained speedup is used to process extra video frames in a video retrieval benchmark, the accuracy of visual categorization is improved by 29%. |
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ISE |
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no |
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Call Number |
Admin @ si @ SGS2011b |
Serial |
1729 |
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Permanent link to this record |
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Author |
Jordi Vitria; Joao Sanchez; Miguel Raposo; Mario Hernandez |
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Title |
Pattern Recognition and Image Analysis |
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Book Whole |
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Year |
2011 |
Publication |
5th Iberian Conference Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
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Las Palmas de Gran Canaria. Spain |
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Springer-Verlag |
Place of Publication |
Berlin |
Editor |
J. Vitrià; J. Sanchez; M. Raposo; M. Hernandez |
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978-3-642-2125 |
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IbPRIA |
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Notes |
OR;MV |
Approved |
no |
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Call Number |
Admin @ si @ VSR2011 |
Serial |
1730 |
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Author |
Jon Almazan; Ernest Valveny; Alicia Fornes |
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Title |
Deforming the Blurred Shape Model for Shape Description and Recognition |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
1-8 |
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Abstract |
This paper presents a new model for the description and recognition of distorted shapes, where the image is represented by a pixel density distribution based on the Blurred Shape Model combined with a non-linear image deformation model. This leads to an adaptive structure able to capture elastic deformations in shapes. This method has been evaluated using thee different datasets where deformations are present, showing the robustness and good performance of the new model. Moreover, we show that incorporating deformation and flexibility, the new model outperforms the BSM approach when classifying shapes with high variability of appearance. |
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Las Palmas de Gran Canaria. Spain |
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Springer-Verlag |
Place of Publication |
Berlin |
Editor |
Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
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LNCS |
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IbPRIA |
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DAG; |
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no |
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Call Number |
Admin @ si @ AVF2011 |
Serial |
1732 |
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Permanent link to this record |
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Author |
Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich |
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Title |
Perception Based Representations for Computational Colour |
Type |
Conference Article |
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Year |
2011 |
Publication |
3rd International Workshop on Computational Color Imaging |
Abbreviated Journal |
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Volume |
6626 |
Issue |
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Pages |
16-30 |
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Keywords |
colour perception, induction, naming, psychophysical data, saliency, segmentation |
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Abstract |
The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space. |
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Milan, Italy |
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Springer-Verlag |
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Editor |
Raimondo Schettini, Shoji Tominaga, Alain Trémeau |
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LNCS |
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978-3-642-20403-6 |
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CCIW |
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CIC |
Approved |
no |
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Call Number |
Admin @ si @ VMB2011 |
Serial |
1733 |
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Permanent link to this record |
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Author |
Michal Drozdzal; Santiago Segui; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria; Petia Radeva |
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Title |
Interactive Labeling of WCE Images |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
143-150 |
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Abstract |
A high quality labeled training set is necessary for any supervised machine learning algorithm. Labeling of the data can be a very expensive process, specially while dealing with data of high variability and complexity. A good example of such data are the videos from Wireless Capsule Endoscopy. Building a representative WCE data set means many videos to be labeled by an expert. The problem that occurs is the data diversity, in the space of the features, from different WCE studies. That means that when new data arrives it is highly probable that it will not be represented in the training set, thus getting a high probability of performing an error when applying machine learning schemes. In this paper an interactive labeling scheme that allows reducing expert effort in the labeling process is presented. It is shown that the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of the WCE video with less than 100 clicks |
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Las Palmas de Gran Canaria. Spain |
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Springer |
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Editor |
Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario |
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IbPRIA |
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Notes |
MILAB;OR;MV |
Approved |
no |
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Call Number |
Admin @ si @ DSM2011 |
Serial |
1734 |
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Permanent link to this record |
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Author |
Lluis Pere de las Heras; Gemma Sanchez |
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Title |
And-Or Graph Grammar for Architectural Floorplan Representation, Learning and Recognition. A Semantic, Structural and Hierarchical Model |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
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17-24 |
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Abstract |
This paper presents a syntactic model for architectural floor plan interpretation. A stochastic image grammar over an And-Or graph is inferred to represent the hierarchical, structural and semantic relations between elements of all possible floor plans. This grammar is augmented with three different probabilistic models, learnt from a training set, to account the frequency of that relations. Then, a Bottom-Up/Top-Down parser with a pruning strategy has been used for floor plan recognition. For a given input, the parser generates the most probable parse graph for that document. This graph not only contains the structural and semantic relations of its elements, but also its hierarchical composition, that allows to interpret the floor plan at different levels of abstraction. |
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Las Palmas de Gran Canaria. Spain |
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978-3-642-21256-7 |
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IbPRIA |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ HeS2011 |
Serial |
1736 |
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Permanent link to this record |
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Author |
Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados |
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Title |
Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval |
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Conference Article |
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Year |
2011 |
Publication |
33rd European Conference on Information Retrieval |
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Volume |
6611 |
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314-325 |
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In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset. |
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Address |
Dublin, Ireland |
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Springer |
Place of Publication |
Berlin |
Editor |
P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch |
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LNCS |
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978-3-642-20160-8 |
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ECIR |
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Notes |
DAG; RV;ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RAK2011 |
Serial |
1737 |
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Permanent link to this record |
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Author |
David Fernandez; Josep Llados; Alicia Fornes |
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Title |
Handwritten Word Spotting in Old Manuscript Images Using a Pseudo-Structural Descriptor Organized in a Hash Structure |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
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628-635 |
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There are lots of historical handwritten documents with information that can be used for several studies and projects. The Document Image Analysis and Recognition community is interested in preserving these documents and extracting all the valuable information from them. Handwritten word-spotting is the pattern classification task which consists in detecting handwriting word images. In this work, we have used a query-by-example formalism: we have matched an input image with one or multiple images from handwritten documents to determine the distance that might indicate a correspondence. We have developed an approach based in characteristic Loci Features stored in a hash structure. Document images of the marriage licences of the Cathedral of Barcelona are used as the benchmarking database. |
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Address |
Las Palmas de Gran Canaria. Spain |
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Editor |
Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
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978-3-642-21256-7 |
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IbPRIA |
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DAG |
Approved |
no |
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Call Number |
Admin @ si @ FLF2011 |
Serial |
1742 |
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Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Dimensionality Reduction for Graph of Words Embedding |
Type |
Conference Article |
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Year |
2011 |
Publication |
8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition |
Abbreviated Journal |
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Volume |
6658 |
Issue |
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Pages |
22-31 |
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Abstract |
The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs. |
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Address |
Münster, Germany |
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Editor |
Xiaoyi Jiang; Miquel Ferrer; Andrea Torsello |
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LNCS |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-3-642-20843-0 |
Medium |
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Area |
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Expedition |
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Conference |
GbRPR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ GVB2011a |
Serial |
1743 |
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Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Vocabulary Selection for Graph of Words Embedding |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
216-223 |
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Keywords |
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Abstract |
The Graph of Words Embedding consists in mapping every graph in a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. It has been shown to perform well for graphs with discrete label alphabets. In this paper we extend the methodology to graphs with n-dimensional continuous attributes by selecting node representatives. We propose three different discretization procedures for the attribute space and experimentally evaluate the dependence on both the selector and the number of node representatives. In the context of graph classification, the experimental results reveal that on two out of three public databases the proposed extension achieves superior performance over a standard reference system. |
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Address |
Las Palmas de Gran Canaria. Spain |
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Corporate Author |
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Thesis |
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Publisher |
Springer |
Place of Publication |
Berlin |
Editor |
Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-3-642-21256-7 |
Medium |
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Area |
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Expedition |
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Conference |
IbPRIA |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ GVB2011b |
Serial |
1744 |
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Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny; Oriol Ramos Terrades; Horst Bunke |
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Title |
Multiple Classifiers for Graph of Words Embedding |
Type |
Conference Article |
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Year |
2011 |
Publication |
10th International Conference on Multiple Classifier Systems |
Abbreviated Journal |
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Volume |
6713 |
Issue |
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Pages |
36-45 |
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Keywords |
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Abstract |
During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs. The graph of words embedding assigns a feature vector to every graph by counting unary and binary relations between node representatives and combining these pieces of information into a single vector. Selecting different node representatives leads to different vectorial representations and therefore to different base classifiers that can be combined. We experimentally show how this methodology significantly improves the classification of graphs with respect to single base classifiers. |
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Address |
Napoles, Italy |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Editor |
Carlo Sansone; Josef Kittler; Fabio Roli |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-3-642-21556-8 |
Medium |
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Area |
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Expedition |
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Conference |
MCS |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @GVR2011 |
Serial |
1745 |
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Permanent link to this record |