A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
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This paper proposes a model of probabilistic learning of object categories in conjunction with early visual processes of attention, segmentation and perceptual organization. This model consists of the following three sub-models: (1) a model of attention-mediated perceptual organization of segments, (2) a model of local feature representation of segments by using a bag of features, and (3) a model of learning object composition of categories based on intra-categorical probabilistic latent component analysis with variable number of classes and intercategorical typicality analysis. Through experiments by using images of plural categories in an image database, it is shown that the model learns a probabilistic structure of intra-categorical composition of objects and context and inter-categorical difference.