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A multiple hypothesis tracking (MHT) algorithm based on multi-feature fusion is presented in this paper to counter range deception jammings. Sparse decomposition coefficients and bispectrum features are extracted to distinguish the targets and the jammings. A two-stage fusion structure using neural network and Dempster-Shafer evidence theory is designed to implement multi-feature fusion so as to get...
To recognize people in unconstrained video, one has to explore the identity information in multiple frames and the accompanying dynamic signature. These identity cues include face, body, and motion. Our approach is based on video-dictionaries for face and body. Video-dictionaries are a generalization of sparse representation and dictionaries for still images. We design the video-dictionaries to implicitly...
In this paper, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries and clusters images in the radon transform domain. The main feature of the proposed approach is that it provides both in-plane rotation and scale invariant clustering, which is useful in numerous applications, including content-based image retrieval (CBIR)...
In this paper, we present an approach that simultaneously clusters database members and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image domain. Themain feature of the proposed approach is that it provides rotation invariant clustering which is useful in Content Based Image Retrieval (CBIR). We demonstrate through experimental...
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