Adaptive relevance feedback model selection for content-based image retrieval

Abstract

Owing to the rapid development in computer and network technologies, the volumes of modern image repositories have been overwhelming. In this context, traditional image retrieval based on textual indexing is laborious, thus inviting the implementation of content-based image retrieval (CBIR). Relevance feedback (RF) is an iterative procedure which refines the content-based retrievals utilizing the user's RF marked on retrieved results. Recent research has focused on RF model space optimisation. In this paper, we propose an adaptive RF model selection framework which automatically chooses the best RF model with proper parameter values for the given query. The proposed method combines the visual space and model space approaches in order to simultaneously perform two learning tasks, namely, the query optimisation and model optimisation. The particle swarm optimisation (PSO) paradigm is applied to assist the learning tasks. Experimental results tested on a real-world image database reveal that the proposed method outperforms several existing RF approaches using different techniques. The convergence behaviour of the proposed method is empirically analysed.

Keywords:

• CONTENT-BASED IMAGE RETRIEVAL
• RELEVANCE FEEDBACK
• LONG-TERM LEARNING
• MODEL SELECTION
• PARTICLE SWARM OPTIMISATION