Pursuit Reinforcement Competitive Learning: PRCL based Online Clustering with Tracking Algorithm and its Application to Image Retrieval

Abstract

Pursuit Reinforcement guided Competitive Learning: PRCL based on relatively fast online clustering that allows grouping the data in concern into several clusters when the number of data and distribution of data are varied of reinforcement guided competitive learning is proposed. One of applications of the proposed method is image portion retrievals from the relatively large scale of the images such as Earth observation satellite images. It is found that the proposed method shows relatively fast on the retrievals in comparison to the other existing conventional online clustering such as Vector Quatization: VQ. Moreover, the proposed method shows much faster than the others for the multi-stage retrievals of image portion as well as scale estimation. A new approach for online clustering based on reinforcement learning, called Pursuit Reinforcement Guided Competitive Learning. PRCL which is derived from pursuit method in reinforcement learning that maintain both action- value and action preferences, with the preferences continually pursuing the action that is greedy according to the current action-value estimates together with learning automata is proposed. PRCL can be used as online clustering method. One of the applications is, then introduced for evacuation simulation. The following section describes the proposed PRCL with learning automata together with the existing conventional online clustering methods of RGCL, SRGCL and VQ. Then preliminary experiments are described followed by its application of image retrievals. After all, conclusion is described with some discussions.