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Abstract—Development of content-based image retrieval (CBIR) techniques has suffered from the lack of standardized ways for describing visual image content. Luckily, the MPEG-7, or formally “Moving Pictures Expert Group Multimedia Content Description Interface ” international standard is now emerging as both a general framework for content description and a collection of specific agreed-upon content descriptors. We have developed a neural, self-organizing technique for CBIR. Our system is named PicSOM and it is based on pictorial examples and relevance feedback (RF). The name stems from “picture ” and the self-organizing map (SOM). The PicSOM system is implemented by using tree structured SOMs. In this paper, we apply the visual content descriptors provided by MPEG-7 in the PicSOM system and compare our own image indexing technique with a reference system based on vector quantization (VQ). The results of our experiments show that the MPEG-7-defined content descriptors can be used as such in the PicSOM system even though Euclidean distance calculation, inherently used in the PicSOM system, is not optimal for all of them. Also, the results indicate that the PicSOM technique is a bit slower than the reference system in starting to find relevant images. However, when the strong RF mechanism of PicSOM begins to function, its retrieval precision exceeds that of the reference system. Index Terms—Content-based image retrieval (CBIR), MPEG-7, query by pictorial example (QBPE), relevance feedback (RF), selforganizing map (SOM), visual content description. I.

In this article the use of shape features in content-based image retrieval is studied. The emphasis is on such techniques which do not demand object segmentation. PicSOM, the image retrieval system used in the experiments, requires that features are represented by constant-sized feature vectors for which the Euclidean distance can be used as a similarity measure. The shape features suggested here are edge histograms and Fourier-transform-based features computed for an edge image in Cartesian and polar coordinate planes. The results show that both local and global shape features are important clues of shapes in an image. 1. Introduction Content-based image retrieval has been an active research area since the early 1990's. Many image retrieval systems, both commercial and research, have been built. The best known are Query By Image Content (QBIC) [3] and Photobook [10] and its new version FourEyes. Other well-known systems are the search engine family VisualSEEk, MetaSEEk and WebSEEk [...

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The aim of the thesis was to study the use of shape features in content-based image retrieval and to implement shape features to such a system called PicSOM. Because of the internal structure and principles of PicSOM the emphasis was put on such shape feature techniques which can be represented by constant-sized feature vectors and with which the Euclidean distance can be used as a similarity measure. The review covers the best-known shape description techniques which have been published in scientic journals and conference proceedings. Based on the review, some techniques were selected for the...

Abstract—This paper presents a novel method for image similarity measure, where a hand-drawn rough black and white sketch is compared with an existing data base of full color images (art works and photographs). The proposed system creates ambient intelligence in terms of the evaluation of nonprecise, easy to input sketched information. The system can then provide the user with options of either retrieving similar images in the database or ranking the quality of the sketch against a given standard, i.e., the original image model. Alternatively, the inherent pattern-matching capability of the system can be utilized to allow detection of distortion in any given real time-image sequences in vision-driven ambient intelligence applications. The proposed method can cope with images containing several images are obtained using strong edges of the model image and the morphologically thinned outline of the sketched image. The angular-spatial distribution of pixels in the abstract images is then employed to extract new compact and effective features using the Fourier transform. The extracted features are rotation and scale invariant and robust against translation. Experimental results from seven different approaches confirm the efficacy of the proposed method in both the retrieval performance and the time required for feature extraction and search. Index Terms—Angular partitioning, Fourier transform, image matching, invariant properties, nonprecise interface, sketched images. I.

Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. In CBIR images are indexed on the basis of low-level features, such as color, texture, and shape, that can automatically be derived from the visual content of the images. The operation of a CBIR system can be seen as a series of more or less independent processing stages. As there exists multiple choices for each of these stages, a multitude of CBIR systems can be implemented by combining a set of common building blocks. In this paper, we present a comparison of different techniques for three consecutive stages of a CBIR system. These include: (1) the initial per feature selection of considered images, (2) the combination of the lists of selected images, and (3) the final selection of images based on all available features simultaneously. The results of the performed experiments show that CBIR systems can be implemented using consecutive stages where, at each stage, a number of parallel techniques can be provided.

Summary. We have developed a method that utilizes hypertext link information in image retrieval from the World Wide Web. The basis of the method consists of a set of basic relations that can take place between two images in the Web. Our method uses the SHA-1 message digest algorithm for dimension reduction by random mapping. The Web link features have then been used in creating a Self-Organizing Map of images in the Web. The method has been effectively tested with our PicSOM content-based image retrieval system using a Web image database containing over a million images. The method can as such be used also in other Web applications not related to content-based image retrieval. 1

Visual Analytics seeks to combine automatic data analysis with visualization and human-computer interaction facilities to solve analysis problems in applications characterized by occurrence of large amounts of complex data. The financial data analysis domain is a promising field for research and application of Visual Analytics technology, as it prototypically involves the analysis of large data volumes in solving complex analysis tasks. We introduce a Visual Analytics system for supporting the analysis of large amounts of financial time-varying indicator data. A system, driven by the idea of extending standard technical chart analysis from one to two-dimensional indicator space, is developed. The system relies on an unsupervised clustering algorithm combined with an appropriately designed movement data visualization technique. Several analytical views on the full market and specific assets are offered for the user to navigate, to explore, and to analyze. The system includes automatic screening of the potentially large visualization space, preselecting possibly interesting candidate data views for presentation to the user. The system is applied to a large data set of time varying 2-D stock market data, demonstrating its effectiveness for visual analysis of financial data. We expect the proposed techniques to be beneficial in other application areas as well.

One of the challenges in the development of a content-based multimedia indexing and retrieval application is to achieve an efficient indexing scheme. The developers and users who are accustomed to making queries to retrieve a particular multimedia item from a large scale database can be frustrated by the long query times. Conventional indexing structures cannot usually cope with the requirements of a multimedia database, such as dynamic indexing or the presence of high-dimensional audiovisual features. Such structures do not scale well with the ever increasing size of multimedia databases whilst inducing corruption and resulting in an over-crowded indexing structure. This paper addresses such problems and presents a novel indexing technique, Hierarchical Cellular Tree (HCT), which is designed to bring an effective solution especially for indexing large multimedia databases. Furthermore it provides an enhanced browsing