Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
An Approach for Preparing Groundtruth Data and Evaluating Visual Saliency Models
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Salient region extraction based on intensity mapping for image retrieval
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Extracting salient visual attention regions by color contrast and wavelet transformation
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
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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