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In this paper, we propose a novel 3D ear classification scheme, making use of the label consistent K-SVD (LC-KSVD) framework. As an effective supervised dictionary learning algorithm, LC-KSVD learns a compact discriminative dictionary for sparse coding and a multi-class linear classifier simultaneously. To use LC-KSVD, one key issue is how to extract feature vectors from 3D ear scans. To this end,...
With the rapid development of the usage of digital imaging and communication technologies, there appears to be a great demand for fast and practical approaches for image quality assessment (IQA) algorithms that can match human judgements. In this paper, we propose a novel general-purpose no-reference IQA (NR-IQA) framework by means of learning quality-aware filters (QAF). Using these filters for image...
This paper proposes a new manifold entropy function based on local tangent space (LTS). With this entropy function, we further propose a framework for image retrieval. The retrieval is treated as searching for ordered cycles by categories in image datasets. The optimal cycles can be found by minimizing our manifold entropy of images.
This paper proposes a method of constructing a contour-based classifier to remove the false positive objects after Haar-based detection. The classifier is learned with the discrete AdaBoost. During the training, the oriented chamfer is introduced to construct strong learners. Experimental results have demonstrated that the proposed method is feasible and promising in the removal of the false positive.
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