Semantic personal image classification by energy expenditure

Abstract

Semantic personal image classification is an attention problem in multimedia image retrieval. In our previous work [Chinpanchana, S et al., 2004], we classified semantic images into business, leisure, and sport categories by integrating the frequency pattern relationships between body parts and objects. However, the accuracy mainly depends on their objects. In the images that have high semantic complexities, the body movement play important solve on the meaning of image. In this paper, we present a new model to achieve more effective classifier called an energy expenditure model (EE). The EE model is based on the concept that human subjects in different classes of images are likely to spend different amounts of energy. The angular position and flexion forces are related into each body part. Experimental results show that the EE a can achieve an improvement of semantic images.