Region-based Retrieval: Coarse Segmentation with Fine Signature (0)

by J Fauqueur, N Boujemaa
Venue:in IEEE International Conference on Image Processing (ICIP) 2002
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New Image Retrieval Paradigm: Logical Composition of Region Categories

by J. Fauqueur, N. Boujemaa, Nozha Boujemaa - In: ICIP03. (2003) III: 601–604 , 2003
"... We present a novel framework for intelligent search and retrieval by image content composition. Very different from the existing Query-by-Example paradigm, logical queries are expressed using categories of similar regions without any starting example region. The set of region category representative ..."
Abstract - Cited by 16 (6 self) - Add to MetaCart
We present a novel framework for intelligent search and retrieval by image content composition. Very different from the existing Query-by-Example paradigm, logical queries are expressed using categories of similar regions without any starting example region. The set of region category representatives constitutes the "photometric region thesaurus" of the image database.
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Region-Based Image Retrieval: Fast Coarse Segmentation and Fine Color Description

by Julien Fauqueur, Nozha Boujemaa - Journal of Visual Languages and Computing (JVLC), special issue on Visual Information Systems , 2003
"... The two major problems raised by a region-based image retrieval system are the automatic detection and visual description of regions. We adopt a coarse detection and fine description approach. In this paper we first present a new method of unsupervised coarse detection which provides intuitive and v ..."
Abstract - Cited by 16 (3 self) - Add to MetaCart
The two major problems raised by a region-based image retrieval system are the automatic detection and visual description of regions. We adopt a coarse detection and fine description approach. In this paper we first present a new method of unsupervised coarse detection which provides intuitive and visually characteristic regions of interest. This segmentation scheme is based on the classification of Local Distributions of Quantized Colors (LDQC). The Competitive Agglomeration classification algorithm is used which has the advantage to automatically determine the number of classes.
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What's Beyond Query By Example?

by N. Boujemaa, J. Fauqueur, Valérie Gouet , 2003
"... Over the last ten years, the crucial problem of information retrieval in multimedia documents has boosted research activities in the fieM of visual appearance indexing and retrieval by content. In the early research years, the concept of the "query by visual example" (QB FE) has been propo ..."
Abstract - Cited by 9 (2 self) - Add to MetaCart
Over the last ten years, the crucial problem of information retrieval in multimedia documents has boosted research activities in the fieM of visual appearance indexing and retrieval by content. In the early research years, the concept of the "query by visual example" (QB FE) has been proposed and shown to be relevant for visual information retrieval. It is obvious that QBVE is not able to satisJ the multiple visual search usage requirements. In this paper, we focus on two major approaches that correspond to two different retrieval paradigms. First, we present the partial visual query that ignores the background of the images and allows a straight user expression on its visual interest without relevance feedback mechanism. The second retrieval paradigm consists in searching for the user mental target image when no starting visual example is available. A visual thesaurus is generated and allows query by logical composition of region categories. This query paradigm is closely related to that of text retrieval.
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Tangible image query

by Thomas Psik, Ina Wagner - In Smart Graphics 2004 , 2004
"... This paper introduces a tangible user interface for browsing and retrieving images from an image database. The basis for the query to the image database is a color layout sketch, which is used by the underlying query algorithm to find the best matches. The users are provided with colored cubes of va ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
This paper introduces a tangible user interface for browsing and retrieving images from an image database. The basis for the query to the image database is a color layout sketch, which is used by the underlying query algorithm to find the best matches. The users are provided with colored cubes of various sizes (1.5 to 4 cm) and colors (8 base colors). The users can place and arrange the colored cubes on a small table to create a color layout sketch. Multiple users can use this interface to collaborate in an image query. To evaluate the benefits of the interface, it is compared to a traditional GUI application in which the users use a mouse to create a color layout sketch. Author Keywords Tangible user interface, image retrieval.
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Logical Query Composition From Local Visual Feature Thesaurus

by J. Fauqueur, N. Boujemaa, Nozha Boujemaa - In Third International Workshop on Content-Based Multimedia Indexing (CBMI’03 , 2003
"... We present a novel framework for intelligent search and retrieval by image region composition. Unlike traditional Query-by-Example paradigm, no starting example image is used : the user provides its mental representation of target image by means of a region photometric thesaurus. It gives an overvie ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
We present a novel framework for intelligent search and retrieval by image region composition. Unlike traditional Query-by-Example paradigm, no starting example image is used : the user provides its mental representation of target image by means of a region photometric thesaurus. It gives an overview of the database content in the query interface.
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doi:10.1155/2008/231930 Research Article Multiple Moving Object Detection for Fast Video Content Description in Compressed Domain

by Francesca Manerba, Jenny Benois-pineau, Riccardo Leonardi, Boris Mansencal
"... Indexing deals with the automatic extraction of information with the objective of automatically describing and organizing the content. Thinking of a video stream, different types of information can be considered semantically important. Since we can assume that the most relevant one is linked to the ..."
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Indexing deals with the automatic extraction of information with the objective of automatically describing and organizing the content. Thinking of a video stream, different types of information can be considered semantically important. Since we can assume that the most relevant one is linked to the presence of moving foreground objects, their number, their shape, and their appearance can constitute a good mean for content description. For this reason, we propose to combine both motion information and regionbased color segmentation to extract moving objects from an MPEG2 compressed video stream starting only considering lowresolution data. This approach, which we refer to as “rough indexing, ” consists in processing P-frame motion information first, and then in performing I-frame color segmentation. Next, since many details can be lost due to the low-resolution data, to improve the object detection results, a novel spatiotemporal filtering has been developed which is constituted by a quadric surface modeling the object trace along time. This method enables to effectively correct possible former detection errors without heavily increasing the computational effort. Copyright © 2008 Francesca Manerba et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
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A Region Based Image Matching Method with Regularized SAR Model

by Yaowei Wang, Weiqiang Wang, Yanfei Wang
"... Abstract. In this paper, we propose a new region-based image match-ing method to find the user defined regions in other images. We use color histogram and SAR (simultaneous autoregressive) model parameters as matching features. We characterize the spatial structure of image region with its block fea ..."
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Abstract. In this paper, we propose a new region-based image match-ing method to find the user defined regions in other images. We use color histogram and SAR (simultaneous autoregressive) model parameters as matching features. We characterize the spatial structure of image region with its block features, and we match the image region in target images with spatial constraints. SAR model was usually used to characterize the spatial interactions among neighboring pixels. But the spectrum of the transition matrix G in the SAR model is not well distributed. Therefore in this paper, we use a regularized SAR model to characterize the spa-tial interactions among neighboring image blocks, which is based on the solution of a penalized LSE (Least Squares Estimation) for computing SAR model parameters. The experimental results show that our method is effective. 1