Image collections are growing at a rapid rate, motivating the need for efficient and effective tools to query these databases. Contentbased image retrieval (CBIR) techniques extract features directly from image data and use these, coupled with a similarity measure, to search through image collections. In this paper, we introduce some of the basic image features that are used for CBIR.

Content-based Image Retrieval (CBIR) systems consider only a pairwise analysis, i.e., they measure the similarity between pairs of images, ignoring the rich information encoded in the relations among several images. However, the user perception usually considers the query specification and responses in a given context. In this scenario, re-ranking methods have been proposed to exploit the conte...

Content-based image retrieval (CBIR) is a new but in recent years widely-adopted method for nding images from vast and unanno-tated image databases. CBIR is a technique for querying images on the basis of automatically-derived features such as color, texture, and shape directly from the visual content of images. For the development of eeec-tive image retrieval applications, one of the most urge...

In this paper, a survey of content based image retrieval techniques is given. Image retrieval method based on shape is analyzed primarily, the description and match based on shape are discussed and compared. And relevance feedback is also introduced. At last we put forward the problems in this field and suggest the directions of future

1 This work was supported in part by NASA grant NAG5-12025 and by the National Science Foundation. The views expressed here are those of the authors and do not necessarily represent those of NASA or the National Science Foundation. ABSTRACT Content-based image retrieval (CBIR) uses features that can be extracted from the images themselves. In previous work we have shown that using more than one...

This paper presents a robust technique for Content Based Image Retrieval (CBIR) using fuzzy edge map of an image. Fuzzy compactness vector is computed from fuzzy edge map thresholded at different levels of the unsegmented image, which also incorporates gray level contrast information embedded in the edges. The resemblance of two images is defined as the similarity between the computed feature v...

This paper presents a CBIR system that is based on a segmented representation of image content. It compares regional features using fuzzy similarity, which have been shown to be psychologically intuitive. We show that they can be aggregated to support four different type of original queries. The system also supports competitive queries to test different visual comparison measures, and lets the ...

In this paper, we propose new texture features, block difference of inverse probabilities (BDIP) and block variation of local correlation coefficients (BVLC), for content-based image retrieval and then present an image retrieval method based on the combination of BDIP and BVLC moments. BDIP uses local probabilities in image blocks to measure local brightness variations of an image well. BVLC us...