Modeling Visual Attention Based on Gestalt Theory

Guang-Hai Liu, Jing-Yu Yang

Abstract: Gestalt theory laid the foundation for modern cognitive learning theory and emphasizes that the whole is greater than the sum of its parts, where similarity and proximity are two important principles. However, exploiting Gestalt theory to detect multiple salient objects remains challenging. In this paper, we propose a very simple yet efficient saliency model based on Gestalt theory, namely, the color similarity and spatial proximity (CSSP) model. It utilizes content-based image retrieval (CBIR) techniques to detect salient objects. The methodology has three important highlights: (1) a novel weighted distance is proposed to calculate spatial proximity. It can control spatial proximity within a certain range and detect salient objects robustly. (2) Two novel and efficient saliency scoring calculation methods are proposed under the framework of CBIR techniques, where color similarity and spatial proximity are used for image matching and the ordering of retrieved images. This enables the robust identification of multiple salient objects. (3) A very simple yet efficient integration method is proposed to combine saliency maps. Using this integration method, impurities around salient objects are greatly reduced, and their interiors are highlighted robustly. Experiments with several well-known benchmark datasets validate the performance of the CSSP model. The CSSP method resulted in fewer grey patches inside salient objects, and it is superior to many existing state-of-the-art methods. The detected salient regions were brighter, improving the effectiveness of multiple salient objects detection. In addition, the CSSP method can detect salient objects robustly even when they touch the image boundaries. It has demonstrated that modeling visual attention based on Gestalt theory is a novel, viable approach.

External IDs:doi:10.1007/s12559-025-10410-8