Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification

22 Jan 2014Shu KongZhuolin JiangQiang Yang

We now know that mid-level features can greatly enhance the performance of image learning, but how to automatically learn the image features efficiently and in an unsupervised manner is still an open question. In this paper, we present a very efficient mid-level feature learning approach (MidFea), which only involves simple operations such as $k$-means clustering, convolution, pooling, vector quantization and random projection... (read more)

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