Image classification based on hash codes and space pyramid

Abstract

Sparse Coding is a widely used method to represent an image. However, sparse coding and its improved algorithms have the problem of complex computation and long running time and so on. For these problems, we propose an image classification method based on hash codes and space pyramid, which encodes local feature points with hash codes instead of sparse coding. Firstly, extract the local feature points from the images. Second, learn binary auto-encoder hashing functions, which map the local feature points into hash codes. Third, perform binary k-means cluster on the binary hash codes and generate the binary visual vocabularies. Finally, Combine with spatial pyramid matching model, and represent the image by the histogram vector of space pyramid, which is used in image classification. Experimental results show that compared with other sparse coding methods, our method has the shorter time of learning vocabularies and faster encoder speed and higher classification accuracy.