The demand of browsing information from general Web pages using a mobile phone is increasing. However, since the majority of Web pages on the Internet are optimized for browsing from PCs, it is difficult for mobile phone users to obtain sufficient information from the Web. Therefore, a method to reconstruct PCoptimized Web pages for mobile phone users is essential. An example approach is to segment the Web page based on its structure, and utilize the hierarchy of the content element to regenerate a page suitable for mobile phone browsing. In our previous work, we have examined a robust automatic Web page segmentation scheme which uses the distance between content elements based on the relative HTML tag hierarchy, i.e., the number and depth of HTML tags in Web pages. However, this scheme has a problem that the content-distance based on the order of HTML tags does not always correspond to the intuitional distance between content elements on the actual layout of a Web page.In this paper, we propose a hybrid segmentation method which segments Web pages based on both the content-distance calculated by the previous scheme, and a novel approach which utilizes Web page layout information. Experiments conducted to evaluate the accuracy of Web page segmentation results prove that the proposed method can segment Web pages more accurately than conventional methods. Furthermore, implementation and evaluation of our system on the mobile phone prove that our method can realize superior usability compared to commercial Web browsers.

No abstract

Numerous efforts on content-based music information retrieval have been presented in recent years. However, the object of such existing research is to retrieve a specific song from a large music database. In this research, we propose a music retrieval method which retrieves songs based on the user's musical preferences. This enables users to discover new songs which they are expected to like. Since music preferences are expected to be highly ambiguous, we propose the implementation of relevance feedback methods to improve the performance of our music information retrieval method. In order to reduce the burden of users to input learning data to the system, we also propose a method to generate user profiles based on genre preferences, and refinement of such profiles based on relevance feedback. Evaluation experiments are conducted based on a corpus of music data with user ratings. Results of these experiments prove the effectiveness of our method.

Document filtering is a task to retrieve documents relevant to a user's profile from a flow of documents. Generally, filtering systems calculate the similarity between the profile and each incoming document, and retrieve documents with similarity higher than a threshold. However, many systems set a relatively high threshold to reduce retrieval of non-relevant documents, which results in the ignorance of many relevant documents. In this paper, we propose the use of a non-relevant information profile to reduce the mistaken retrieval of non-relevant documents. Results from experiments show that this filter has successfully rejected a sufficient number of non-relevant documents, resulting in an improvement of filtering performance.

This paper describes our new algorithm for shot boundary detection and its evaluation. We adopt a 2-stage data fusion approach with SVM technique to decide whether a boundary exists or not within a given video sequence. This approach is useful to avoid huge feature space problems, even when we adopt many promising features extracted from a video sequence. We also introduce a novel feature to improve detection. The feature consists of two kinds of values extracted from a local frame sequence. One is the image difference between the target frame and that synthesized from the neighbors. The other is the difference between neighbors. This feature can be extracted quickly with a least-square technique. Evaluation of our algorithm is conducted with the TRECVID evaluation framework. Our system obtained a high performance at a shot boundary detection task in TRECVID2005.

No abstract

No abstract

No abstract