A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantics-Based Image Retrieval by Region Saliency
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Content Based Image Retrieval through Object Extraction and Querying
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Automatic Identification of Perceptually Important Regions in an Image
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Data Mining
Automatic video object segmentation using volume growing and hierarchical clustering
EURASIP Journal on Applied Signal Processing
Central object extraction for object-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Region-based representations of image and video: segmentation tools for multimedia services
IEEE Transactions on Circuits and Systems for Video Technology
An integrated approach for content-based video object segmentation and retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Video object segmentation: a compressed domain approach
IEEE Transactions on Circuits and Systems for Video Technology
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Extracting objects of interest in video is a challenging task that can improve the performance of video compression and retrieval. Usually moving objects in video were considered as objects of interest, so there were many researches to extract them. However, we know that some non-moving (static) objects also can be objects of interest. A segmentation method is proposed in this paper, which extracts static objects as well as moving objects that are likely to attract human’s interest. An object of interest is defined as the relatively large region that appears frequently over several frames and is not located near boundaries of the frames. A static object of interest should also have significant color and texture characteristics against its surround. We found that the objects of interest extracted by the proposed method were well matched with the objects of interest selected manually.