We propose a novel feature extraction method for image classification.įollowing the BoF approach, a plenty of local descriptors are first extracted in an image and
HISTOGRAM NORMALIZATION MATLAB 2009 CODE
Matlab code Image Feature by Histogram of Oriented p.d.f Gradients IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. On the other hand, in the bag-of-feature (BoF) frame- work, the Dirichlet mixture model can be extended to Gaussian mixture by transforming histogram-based local de- scriptors, e.g., SIFT, and thereby we propose the method of Dirichlet-derived GMM Fisher kernel.ĭirichlet-based Histogram Feature Transform for Image Classification, The method works on the individual histogram feature to enhance the discriminative power at a low computational cost. Based on the probabilistic modeling, we induce the Dirichlet Fisher kernel for transforming the histogram feature vector. The (L1-normalized) histogram feature is regarded as a probability mass function, which is modeled by Dirichlet distribution. In this paper, we propose a method to efficiently transform those histogram features for improving the classification performance. Histogram-based features have significantly contributed to recent development of image classifications, such as by SIFT local descriptors. Spatio-Temporal Auto-Correlation of Gradients (STACOG)ĭirichlet-based Histogram Feature Transform.Image Feature by Histogram of Oriented p.d.f Gradients.
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