<title>Personalized image retrieval with user's preference model</title>

Abstract

Recently, available information resources in the form of various media have been increased with rapid speed. Many retrieval systems for multimedia information resources have been developed only focused on their efficiency and performance. Therefore, they cannot deal with user's preferences and interests well. In this paper, we present the framework design of a personalized image retrieval system (PIRS) which can reflect user's preferences and interests incrementally. The prototype of PIRS consists of two major parts: user's preference model (UPM) and retrieval module (RM). The UPM plays a role of refining user's query to meet with user's needs. The RM retrieves the proper images for refined query by computing the similarities between each image and refined query, and the retrieved images are ordered by these similarities. In this paper, we mainly discuss about UPM. The incremental machine learning technologies have been employed to provide the user adaptable and intelligent capability to the system. The UPM is implemented by decision tree based on incremental tree induction, and adaptive resonance theory network. User's feedbacks are returned to the UPM, and they modify internal structure of the UPM. User's iterative retrieval activities with PIRS cause the UPM to be revised for user's preferences and interests. Therefore, the PIRS can be adapted to user's preferences and interests. We have achieved encouraging results through experiments.