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Graphical models have been employed in a wide variety of computer vision tasks. Assignments of latent variables in typical models usually suffer the confused explanation in sampling way. In this paper we present discriminative sequential association Latent Dirichlet Allocation, a novel statistical model for the task of visual recognition, and especially focus on the case of few training examples....
In this paper, a set of biologically-inspired features are presented for robust object recognition. The proposed pyramidal feature set is obtained by extracting the geometric relationship of keypoints using a set of biologically inspired templates in different scales. Lifetime is proposed to describe the keypoints. This paper brings together new algorithms, representations, and insights which are...
A novel method is presented to improve the object recognition performance of a biologically inspired model by learning class-specific feature codebook. The feature codebook is multi-class shared in the original model, and the content proportion for different codeword type is set in uniform distribution. According to corresponding discriminability, the codebook content proportion is adjusted upon different...
Aiming at class-specific recognition tasks, a novel method is presented to improve the object recognition performance of a biologically inspired model by learning class specific feature codebook. The feature codebook is multi-class shared in the original model, and the content proportion for different codeword type is set in uniform distribution.According to corresponding discriminability, we modify...
To study the object recognition in complex scene, a synergetic object recognition algorithm based on visual attention saliency map is proposed in the paper. We utilize the feature of the object extracted by PCA as the prototype vector of the synergetic pattern recognition. The adjoint vector is calculated through the synergetic learning algorithm. Then, the salient locations of the scene image including...
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