Ka Cheung Sia

Distributed content-based visual information retrieval system on peer-to-peer networks

With the recent advances of distributed computing, the limitation of information retrieval from a centralized image collection can be removed by allowing distributed image data sources to interact with each other for data storage sharing and information retrieval. In this article, we present our design and implementation of DISCOVIR: DIStributed COntent-based Visual Information Retrieval system using the Peer-to-Peer (P2P) Network. We describe the system architecture and detail the interactions among various system modules. Specifically, we propose a Firework Query Model for distributed information retrieval, which aims to reduce the network traffic of query passing in the network. We carry out experiments to show the distributed image retrieval system and the Firework information retrieval algorithm. The results show that the algorithm reduces network traffic while increases searching performance.

Efficient Monitoring Algorithm for Fast News Alerts

Recently, there has been a dramatic increase in the use of XML data to deliver information over the Web. Personal Weblogs, news Web sites, and discussion forums are now publishing RSS feeds for their subscribers to retrieve new postings. As the popularity of personal Weblogs and RSS feeds grows rapidly, RSS aggregation services and blog search engines have appeared, which try to provide a central access point for simpler access and discovery of new content from a large number of diverse RSS sources. In this paper, we study how the RSS aggregation services should monitor the data sources to retrieve new content quickly using minimal resources and to provide its subscribers with fast news alerts. We believe that the change characteristics of RSS sources and the general user access behavior pose distinct requirements that make this task significantly different from the traditional index refresh problem for Web search engines. Our studies on a collection of 10,000 RSS feeds reveal some general characteristics of the RSS feeds and show that, with proper resource allocation and scheduling, the RSS aggregator provides news alerts significantly faster than the best existing approach.

Relevance feedback based on parameter estimation of target distribution

Relevance feedback formulations have been proposed to refine query result in content-based image retrieval in the past few years. Many of them focus on a learning approach to solve the feedback problem. In this paper, we present an expectation maximization approach to estimate the users target distribution through users feedback. Furthermore, we describe how to use the maximum entropy display to fully utilize users feedback information. We detail the process and also demonstrate the result through experiments.