Article

Toward a Semantic Indexing Based on Images Color Features Similarity?

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Abstract

INTRODUCTION 1.1. In search of the semantic content of images Content-based image retrieval (CBIR) has been a widely studied issue [1,2,3], among multimedia database management techniques. Image databases managementhave applications in various areas, such as medical databases, satellite images, photo-journalism, art, or industry, with a growing demand linked to the development of digital image libraries on Internet. Direct search and query by human users in image databases are based on the semantic content of the image, assuming that one is able to extract from an image a compact semantic representation of the image through a signature, and to apply on the extracted signature a similarity measure which is perceptually signicant[4]. Wemust admit that it is still an open research area [5,6]. Beyond the sensitive issue of image retrieval of still images, many other applications to video documents rely on the concept of similarity. The

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... In addition, unlike RGB counterpart, HLS color space is perceptually uniform and studies in Veneau et al. (2001) demonstrate that the HLS color model performs better than the RGB color model in matching video images, a property strongly desired in many parts of this work. The HLS color model comprises of Hue (H), Lightness (L) and Saturation (S). ...
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Cet article présente une structure d’index pour la recherche d’images par le contenu, l’arbre QUIP (acronyme anglais pour Quadtree-based Index for Image Retrieval and Image Pattern search). Dans notre approche, chaque image de la base est représentée par un descrip- teur dit multi-niveau, qui stocke les descripteurs des quadrants de l’image, obtenus par une décomposition de l’image en arbre quaternaire. L’arbre QUIP permet de regrouper les images en clusters, en fonction de la similarité de leurs quadrants. Cette structure d’index permet non seulement des recherches globales d’images par le contenu, en appliquant un filtrage multi- niveau via l’arbre quaternaire, mais aussi des recherches d’images similaires par région. This article presents a quadtree-based data structure for effective indexing of images. An image is represented by a multi-level feature vector , computed by a recursive decomposition of the image into four quadrants and stored as a full fixed-depth balanced quadtree. A node of the quadtree stores a feature vector of the corresponding image quadrant. A more general quadtree-based structure called QUIP-tree (QUadtree-based Index for image retrieval and Pat- tern search) is used to index the multi-level feature vectors of the images and their quadrants. A QUIP-tree node is an entry to a set of clusters that groups similar quadrants according to some pre-defined distances. The QUIP-tree allows a multi-level filtering in content-based image retrieval as well as partial queries on images. oui
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