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Content-based Retrieval for Heterogeneous Domains: Domain Adaptation by Relative Aggregation Points by Makoto P. Kato; Hiroaki Ohshima; Katsumi Tanaka is a scholarly article available to read on EtoBox.

What is Content-based Retrieval for Heterogeneous Domains: Domain Adaptation by Relative Aggregation Points about?

We introduce the problem of domain adaptation for content-based retrieval and propose a domain adaptation method based on relative aggregation points (RAPs). Content-based retrieval including image retrieval and spoken document retrieval enables a user to input examples as a query, and retrieves relevant data based on the similarity to the examples. However, input examples and relevant data can be dissimilar, especially when domains from which the user selects examples and from which the system retrieves data are different. In content-based geographic object retrieval, for example, suppose that a user who lives in Beijing visits Kyoto, Japan, and wants to search for relatively inexpensive restaurants serving popular local dishes by means of a content-based retrieval system. Since such restaurants in Beijing and Kyoto are dissimilar due to the difference in the average cost and areas' popular dishes, it is difficult to find relevant restaurants in Kyoto based on examples selected in Beijing. We propose a solution for this problem by assuming that RAPs in different domains correspond, which may be dissimilar but play the same role. A RAP is defined as the expectation of instances in

Author

Makoto P. Kato; Hiroaki Ohshima; Katsumi Tanaka

Publisher

ACM

Published

2012

Language

EN