Results 1 - 10
of
16
Region-based segmentation of images using syntactic visual features
- in WIAMIS ’05
, 2005
"... This paper presents a robust and efficient method for segmentation of images into large regions that reflect the real world objects present in the scene. We propose an extension to the well known Recursive Shortest Spanning Tree (RSST) algorithm based on a new color model and so-called syntactic fea ..."
Abstract
-
Cited by 36 (8 self)
- Add to MetaCart
(Show Context)
This paper presents a robust and efficient method for segmentation of images into large regions that reflect the real world objects present in the scene. We propose an extension to the well known Recursive Shortest Spanning Tree (RSST) algorithm based on a new color model and so-called syntactic features [1]. We introduce practical solutions, integrated within the RSST framework, to structure analysis based on the shape and spatial configuration of image regions. We demonstrate that syntactic features provide a reliable basis for region merging criteria which prevent formation of regions spanning more than one semantic object, thereby significantly improving the perceptual quality of the output segmentation. Experiments indicate that the proposed features are generic in nature and allow satisfactory segmentation of real world images from various sources without adjustment to algorithm parameters. 1.
Mental image search by boolean composition of region categories »,
- Multimedia Tools and Applications,
, 2006
"... Abstract Existing content-based image retrieval paradigms almost never address the problem of starting the search, when the user has no starting example image but rather a mental image. We propose a new image retrieval system to allow the user to perform mental image search by formulating boolean c ..."
Abstract
-
Cited by 17 (2 self)
- Add to MetaCart
(Show Context)
Abstract Existing content-based image retrieval paradigms almost never address the problem of starting the search, when the user has no starting example image but rather a mental image. We propose a new image retrieval system to allow the user to perform mental image search by formulating boolean composition of region categories. The query interface is a region photometric thesaurus which can be viewed as a visual summary of salient regions available in the database. It is generated from the unsupervised clustering of regions with similar visual content into categories. In this thesaurus, the user simply selects the types of regions which should and should not be present in the mental image (boolean composition). The natural use of inverted tables on the region category labels enables powerful boolean search and very fast retrieval in large image databases. The process of query and search of images relates to that of documents with Google. The indexing scheme is fully unsupervised and the query mode requires minimal user interaction (no example image to provide, no sketch to draw). We demonstrate the feasibility of such a framework to reach the user mental target image with two applications : a photo-agency scenario on Corel Photostock and a TV news scenario. Perspectives will be proposed for this simple and innovative framework, which should motivate further development in various research areas.
What's Beyond Query By Example?
, 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
(Show Context)
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.
Sample selection strategies for relevance feedback in region-based image retrieval
- Lecture Notes in Computer Science
, 2004
"... region-based image retrieval ..."
(Show Context)
Perceptual Color Descriptor Based on a Spatial Distribution Model: PROXIMITY HISTOGRAMS
"... Abstract- Color is the main source of information particularly for content-analysis and retrieval. Most of the color descriptors, however, show severe limitations and drawbacks due to their incapability of modelling the human color perception. Moreover, they cannot characterize all the properties of ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
(Show Context)
Abstract- Color is the main source of information particularly for content-analysis and retrieval. Most of the color descriptors, however, show severe limitations and drawbacks due to their incapability of modelling the human color perception. Moreover, they cannot characterize all the properties of the color composition in a visual scenery. In this paper we present a perceptual color feature, which describes all major properties of prominent colors both in spatial and color domain. In accordance with the well-known Gestalt law, we adopt a top-down approach in order to model (see) the whole color composition before its parts and in this way we can avoid the problems of pixel-based approaches. In color domain the dominant colors are extracted along with their global properties and quad-tree decomposition partitions the image so as to characterize the spatial color distribution (SCD). The proposed color model distils the histogram of inter-color distances. Combination of the extracted global and spatial properties forms the final descriptor, which is neither biased nor become noisy from the presence of such color elements that cannot be perceived in both spatial and color domains. Finally a penalty-trio model fuses all color properties in a similarity distance computation during retrieval. Experimental results approve the superiority of the proposed technique against well-known global and spatial descriptors.
Duration
"... This document presents a State Of the Art related to most popular products, tools and methods related to CBIR. ..."
Abstract
- Add to MetaCart
This document presents a State Of the Art related to most popular products, tools and methods related to CBIR.
Instantaneous Mental Image Search
, 2005
"... The Mental Image Search paradigm allows the user to retrieve images which match the target image s/he has in mind without a starting example. We present a novel approach for this paradigm which enables multiple descriptor range-query, which is necessary to match the more or less precise idea of t ..."
Abstract
- Add to MetaCart
The Mental Image Search paradigm allows the user to retrieve images which match the target image s/he has in mind without a starting example. We present a novel approach for this paradigm which enables multiple descriptor range-query, which is necessary to match the more or less precise idea of the user's mental image. In a simple and intuitive way, sophisticated queries can be formulated on the visual appearance of the mental image components.
Dominant Color Extraction based on Dynamic Clustering by Multi-Dimensional Particle Swarm Optimization
- SEVENTH INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING
, 2009
"... Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utter importance since human visual system primarily uses them for perception. In this paper we address dominant color extracti ..."
Abstract
- Add to MetaCart
(Show Context)
Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utter importance since human visual system primarily uses them for perception. In this paper we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multi-Dimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. To address this problem we then present Fractional Global Best Formation (FGBF) technique, which basically collects all promising dimensional components and fractionally creates an artificial global-best particle (aGB) that has the potential to be a better “guide ” than the PSO’s native gbest particle. We finally propose an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.
Perceptual Dominant Color Extraction by Multi-Dimensional Particle Swarm Optimization
- EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, IN PRINT
, 2009
"... Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we ..."
Abstract
- Add to MetaCart
(Show Context)
Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multi-Dimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then present Fractional Global Best Formation (FGBF) technique, which basically collects all promising dimensional components and fractionally creates an artificial global-best particle (aGB) that has the potential to be a better “guide ” than the PSO’s native gbest particle. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-)similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.
