Fusing Multiple Visual Features for Image Complexity Evaluation

Kunyan Yin, Linbo Wang, Yanwen Guo

Abstract: In spite of the wide applications in computer vision and cognitive research area, defining an effective complexity measure for color images remains a challenging task. Conventional approaches are generally built upon information theory or a certain visual feature. In this paper, we propose a new method directly exploiting multiple effective visual features including color, clutter and the number of objects to measure the complexity of color images. Furthermore, we present a fuzzy clustering model for combining all the proposed features, which provides specific scores to evaluate image complexity. Experimental results are presented and show good consistency between the proposed objective metric and subjective assessment by human observers.