A search engine that can handle TV programs and Web content in an integrated way is proposed. Conventional search engines have been able to handle Web content and/or data stored in a PC desktop as target information. In the future, however, the target information is expected to be stored in various places such as in hard-disk (HD)/DVD recorders, digital cameras, mobile devices, and even in real space as ubiquitous content, and a search engine that can search across such heterogeneous resources will become essential. Therefore, as a first step towards developing such next-generation search engine, a prototype search system for Web and TV programs is developed that performs integrated search of those content, and that allows chain search where related content can be accessed from each search result. The integrated search is achieved by generating integrated indices for Web and TV content based on vector space model and by computing similarity between the query and all the content described by the indices. The chain search of related content is done by computing similarity between the selected result and all other content based on the integrated indices. Also, the zoom-based display of the search results enables to control media transition and level of details of the contents to acquire information efficiently. In this paper, testing of a prototype of the integrated search engine validated the approach taken by the proposed method.

We examine the effect of the fuzzy aggregation operators on the image retrieval performance, by empirically comparing 67 operators, applied to the problem of computing the image similarity, given a collection of feature similarities of the image regions. While majority of the existing image similarity models express the image similarity as an aggregation of feature similarities, no study presents a systematic comparison of the different operators. We compare the 67 operators by: (1) incorporating each operator into a hierarchical, region-based similarity model, which expresses the image similarity as an aggregation of region similarities, and each region similarity as an aggregation of the corresponding feature similarities; and (2) evaluating the obtained model(s) on five test databases, containing 64,339 general-purpose images, in 749 semantic categories. Results show that the retrieval performance strongly depends on the operator(s) incorporated in the similarity model - the difference in the average retrieval precision between the best and the worst performing of the 67 operators is up to 50%.

Local similarity pattern (LSP) is proposed as a new method for computing image similarity. Similarity of a pair of images is expressed in terms of similarities of the corresponding image regions, obtained by uniform partitioning of the image area. Different from the conventional methods, each region-wise similarity is computed using a different combination of image features (color, shape, and texture). In addition, a method for optimizing LSP, based on genetic algorithm, is proposed, and incorporated in the relevance feedback process, allowing the user to automatically specify LSP-based queries. LSP is evaluated on four test databases totalling over 2,000 images. Compared with six conventional methods, and SIMPLIcity, an advanced image retrieval system, LSP brings between 15% and 24% increase in the average retrieval precision. LSP, allowing comparison of different image regions using different similarity criteria, is more suited for modeling human perception of image similarity than the conventional methods.