Sparse coding is widely used to represent images. However

this method and its improved algorithms require complex computation and long running times

among other drawbacks. An image classification algorithm

based on hash codes and space pyramids

is proposed to solve these issues. The algorithm consists of four steps. First

extract 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 a spatial pyramid matching model

and represent the image by the histogram vector of the space pyramid

which is used for image classification. In order to verify the efficiency of the proposed algorithm

we used two common datasets

Caltech-101 and Scene-15. The results were compared with state-of-the-art sparse coding algorithms

which showed the time of learning vocabularies of our method was 50% left

the online encoder speed was increased 1.3~12.4 times

and the classification accuracy increased 1%~5%. We also compared the classification performance of different hash encode methods

such as PCA-ITQ and KSH. In this paper

a novel image classification algorithm was proposed. The proposed algorithm

encoded local feature points by hash codes rather than sparse coding

and image classification was achieved with a spatial pyramid matching model. Experimental results showed that the proposed algorithm had faster learning vocabulary and encoder speed

and could be used for online vocabulary learning and other online applications.