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Using hashing algorithms to learn binary codes representation of data for fast approximate nearest neighbor (ANN) search has attracted more and more attentions. Most existing hashing methods employ various hash functions to encode data. The resulting binary codes can be obtained by concatenating bits produced by those hash functions. These methods usually have two main steps: projection and thresholding...
In this paper, we consider the image classification problem. Unlike conventional local learning technique, a novel framework, which is based on the proposed sparsity induced neighbors (SINs) instead of widely used k nearest neighbors, is presented. Within this framework, the SINs of test image are training images associated with the nonzero entries in the sparse representation of test image, and they...
In this paper, we call the pattern classification problem that consists in assigning a category label to a long audio signal based on its semantic content as Generic Audio Document Categorization (GADC). A novel generative model is proposed to describe the generic audio document categories and solve the GADC problem. This model is a four-level hierarchical model in which two latent variables “audio...
We consider the image classification problem based on the similarities between images. The choice of the similarity is related to the particular applications, and it could be based on color, texture, bag-of-features, or even more complex kernels. As long as the pair-wise similarity matrix is transformed into a positive semidefinite one, the similarities of images could be treated as kernels. This...
A novel approach based on the probabilistic latent semantic analysis model (pLSA) for automatic musical genre classification is proposed in this paper. Unlike traditional usage, the pLSA is used to model musical genre instead of single music signal in the proposed approach. First, an unsupervised clustering algorithm is utilized to group temporal segments in music signals into several natural clusters...
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