IEEE Transactions on Pattern Analysis and Machine Intelligence
A Goal Oriented Attention Guidance Model
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Models of bottom-up and top-down visual attention
Models of bottom-up and top-down visual attention
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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Existing attention models concentrate on bottom-up attention guidance, and lack of effective definition of top-down attention information. In this paper we define a new holistic scene representation and use it as top-down attention information which works in three ways. The first is to discriminate between close-up and open scene categories. The second and the third are to provide reliable priors for the presence or absence of object and the location of it. Compared with traditional attention guidance algorithms, our algorithm shows how scene classification and basing directly on entire scene without segmentation stages, facilitate the object detection. Two stages of pre-attention and focus attention enhance the detecting performance and are more suitable for vision information processing in high level. Experiment results prove the effectiveness of our algorithm.