Content_based image retrieval is a promising approach because of its automatic indexing, recognition and retrieval. This paper is a contribution in the field of the content Based Image Retrieval (CBIR). Objects are represented by their outlines shapes (silhouettes) and described following the XLWDOS Textual Description (Larabi et al., 2003). Textual Descriptors are sensitive to noise. The autho...

In this paper we present a region growing approach for color images using an online learning algorithm that aims for content-based image retrieval systems. Our learning algorithm follows a variation of Bayesian estimation procedure to characterize each region as it is grown. The resulting image growing procedure is simple to implement, robust to initial parameters and has a linear complexity. R...

Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo sharing sites are getting more popularity, large human face photos are available i...

This paper presents a robust technique for Content Based Image Retrieval (CBIR) using salient points of an image. The salient points are extracted from different levels of the unsegmented image. Local contrast information at different resolution is embedded along with shape information. Fuzzy compactness vector is computed from the signature obtained at different thresholds. The resemblance of ...

Content based image retrieval (CBIR) has been well studied in the computer vision and multimedia community. Content free image retrieval (CFIR) methods, and their complementary characteristics to CBIR has not received enough attention in the literature. Performance of CBIR is constrained by the semantic gap between the feature representations and user expectations, while CFIR suffers with spars...

Benchmarking Content Based Image Retrieval (CBIR) systems allows researchers and developers to compare the strengths of different approaches, and is an essential step towards establishing the credibility of CBIR systems for commercial applications. Here we introduce the problem of developing a benchmark, discuss some of the issues involved, and provide a review of current and recent benchmarkin...

We propose a Modiied Fourier Descriptor and a new distance measure for describing and comparing closed planar curves. Our method accounts for spatial discretization of shapes, an issue seldom mentioned, much less addressed in the literature. The motivating application is shape matching in the Multimedia Analysis and Retrieval System (MARS), our content-based image retrieval system. The applicat...

Semantic-based image retrieval is the desired target of Content-based image retrieval (CBIR). In this paper, we proposed a new method to extract semantic information for CBIR using the relevance feedback results. Firstly it is assumed that positive and negative examples in relevant feedback are containing semantic content added by users. Then image internal semantic model (IISM) is proposed to ...

In this paper we present image data representation, similarity image retrieval, the architecture of a generic content-based image retrieval system, and different content-based image retrieval systems.

We present a framework for active learning in the multiple-instance (MI) setting. In an MI learning problem, instances are naturally organized into bags and it is the bags, instead of individual instances, that are labeled for training. MI learners assume that every instance in a bag labeled negative is actually negative, whereas at least one instance in a bag labeled positive is actually posit...