A Rule Based Technique for Extraction of Visual Attention Regions Based on Real-Time Clustering

Recently, the detection of visual attention regions (VAR) is becoming more important due to its useful application in the area of multimedia. Although there exist a lot of approaches to detect visual attention regions, few of them consider the semantic gap between the visual attention regions and high-level semantics. In this paper, we propose a rule based technique for the extraction of visual attention regions at the object level based on real-time clustering, such that VAR detection can be performed in a very efficient way. The proposed technique consists of four stages: 1) a fast segmentation technique which is called the real time clustering algorithm (RTCA); 2) a refined specification of VAR which is known as the hierarchical visual attention regions (HVAR); 3) a new algorithm known as the rule based detection algorithm (RADA) to obtain the set of HVARs in real time, and 4) a new adaptive image display module and the corresponding adaptation operations using HVAR. We also define a new background measure which combines both feature contrast and the geometric property of the region to identify the background region, and a confidence factor which is used to extract the set of hierarchical visual attention regions. Compared with existing techniques, our approach has two advantages: 1) the approach detects the visual attention region at the object level, which bridges the gap between traditional visual attention regions and high-level semantics; 2) our approach is efficient and easy to implement