Abstract. This paper deals with content-based image retrieval. When the user is looking for large categories, statistical classification techniques are efficient as soon as the training set is large enough. We introduce a two-step – exploration, classification – interactive strategy designed for category retrieval. The first step aims at getting a useful initial training set for the classification step. A stochastic image selection process is used instead of the usual strategy based on a similarity score ranking. This process is dedicated to explore the database in order to collect examples as various as possible of the searched category. The second step aims at providing the best classification between relevant and irrelevant images. Based on SVM, the classification applies an active learning strategy through user interaction. A quality assessment is carried out on the ANN and COREL databases in order to compare and validate our approach. 1

Information retrieval techniques have to face both the growing amount of data to be processed and the “natural ” distribution of these data over the network. Hence, we introduce in this paper a new architecture for image retrieval in distributed image databases, based on multi-agent systems. Our system, inspired by “ant-agents”, uses labels provided by the user for learning both the searched category of images and the path to the most relevant databases. We then show how effective can be our architecture on a generalist image database network. Index Terms — Image recognition, Information retrieval, Image databases, Distributed database searching, Cooperative systems

Abstract—In this article, we present a framework for distributed content based image retrieval with online learning based on antlike mobile agents. Mobile agents crawl the network to find images matching a given example query. The images retrieved are shown to the user who labels them, following the classical relevant feedback scheme. The labels are used both to improve the similarity measure used for the retrieval and to learn paths leading to sites containing relevant images. The relevant paths are learned in an ethologically inspired way. We made experiments on the trecvid 2005 keyframe dataset showing that learning both the similarity function and the localization of the relevant images leads to a significant improvement. We also present an extension with the reuse of learned paths for later sessions leading to a further improvement. Index Terms—Cooperative systems, image databases, information retrieval.

Abstract — In this paper we mainly focussed on how to retreive the images from large database. Generally in huge databases we will have large number of images with the same name. when we want to retrieve the image by giving a name,we may get some of the images with the same name,but in our method it retrieves the images based on the content in the image and it also show the comparision between present image in the database with the target image we are searching for.This is fast and efficient method for retriving the exact images from huge databases.