Using knowledge of the region of interest (ROI) in automatic image retrieval learning

In this paper, we propose an automatic relevance feedback retrieval system using perceptually important features extracted from regions of interest. The system is implemented via self-learning using a self-organizing tree map (SOTM) neural network. Our proposed method involves the construction of regions of interest from retrieved images using Edge Flow model, and the grouping of the regions into a single perceptually significant entity. This knowledge is fed into a set of unsupervised relevance feedback learning modules based on the SOTM to guide the adaptation of relevance feedback parameters through a machine learning approach without user interaction. Optimal tradeoff between the user workload in the interactive process and user subjectivity is then be explored by incorporating a semi-automatic retrieval strategy. Experimental results indicate that this system, with automatic and semiautomatic adaptations, can minimize user interaction, optimize precision, as well as reduce performance errors caused user subjectivity.