The recent development of computing hardware has resulted in a rapid increase of visual information such as databases of images. To successfully utilize this increasing amount of data, we need eoeective ways to process it. Content-based image retrieval utilizes the visual content of images directly in the process of retrieving relevant images from a database. The retrieval is based on visual features such as the colors, textures, shapes, and spatial relations the image contains rather than traditional textual keywords. These features are usually extracted automatically, without the need for a human operator. In the literature survey part o...

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In this paper, the application of support vector machines (SVM) in relevance feedback for region-based image retrieval is investigated. Both the one class SVM as a class distribution estimator and two classes SVM as a classifier are taken into account. For the latter, two representative display strategies are studied. Since the common kernels often rely on inner product or Lp norm in the input space, they are infeasible in the region-based image retrieval systems that use variable-length representations. To resolve the issue, a new kind of kernel that is a generalization of Gaussian kernel is proposed. Experimental results on a database of 10,000 generalpurpose images demonstrate the effectiveness and robustness of the proposed approach.

Abstract. Nowadays, digital libraries are inherently dispersed over several peers of a steadily increasing network. Dedicated peers may provide specialized, computationally expensive services such as image similarity search. Usually, the peers of such a network are uncoordinated in the sense that their content and services are not linked together. Nevertheless, users expect to transparently access and modify all the (multimedia) content anytime from anywhere not only in an efficient and effective but also consistent way. To match these demands, future digital libraries require an infrastructure that combines various information technologies like (mobile) databases, service-oriented architectures, peer-to-peer and grid computing. In this paper, we sketch such an infrastructure and illustrate how an example digital library application can work atop it. 1

Abstract. The content-based image retrieval (CBIR) system PicSOM uses a variety of low-level visual features as an indexing mechanism for an image database. In this paper we describe the implementation of segmentation into the PicSOM framework. That is, we have modified the system to use image segments as a supplement to entire images in order to improve the retrieval accuracy. In a series of experiments, we compare this new method to the baseline PicSOM system. The results confirm that using both segments and entire images together always increases the precision of retrieval. 1

. Content-based image retrieval systems allow the user to interactively search image databases looking for those images which are similar to a specified query image. To thisend, region-based systems decompose each database image into a set of "homogeneous" regions. Similarity between images is then assessed by computing similarity between regions and combining the results at image level. In this paper we propose a correct approach to region matching maximizing the overall similarity score between images. The presented approach only relies on the assumption that regions' scores are to be combined using a monotonic function. Experimental results obtained using an existing system show the e#ectiveness of our approach with respect to existing region matching heuristics.Then, to reduce query costs, we present an index-based algorithm that can make use of any distance-based access structure, and demonstrate its e#ciency on a medium-size image data-set. 1

In this paper, we first apply the theory of wallpaper groups to natural images and extract a novel feature to depict the symmetry property of natural images. The original proposed algorithm takes autocorrelation and correlation as a preprocessing step, which is very timeconsuming. Through further analysis, we develop a set of schemes to accelerate this algorithm. Experimental results demonstrate that in performing content-based image retrieval, the proposed symmetry feature outperforms wavelet feature, which is a widely accepted descriptor of texture, and water-filling feature. The accelerated version of the algorithm improves the processing speed by a large margin while it brings little degradation to retrieval performance. 1.

Content-based image retrieval (CBIR) systems can be used also for other purposes than online access to unannotated image databases. In particular, when a CBIR system is equipped with an automatic image segmentation subsystem, keyword annotations given on image level can be focused on specific image segments. In this paper, we show that our PicSOM CBIR system is able to reveal semantic knowledge not only from keyword annotations but also from recorded online use of the system. This automatically extracted high abstraction level visual information can then be used to further improve the accuracy of the system and to categorize the objects of the database with semantic concepts. This process, we claim, then helps to bridge the semantic gap between low-level visual features available for computers and the high-level semantic terms used by the humans. The results of the experiments described in this paper support that view. 1.

Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases.

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