Scene Classification Using Local Co-occurrence Feature in Subspace Obtained by KPCA of Local Blob Visual Words

In recent years, scene classification based on local correlation of binarized projection lengths in subspace obtained by Kernel Principal Component Analysis (KPCA) of visual words was proposed and its effectiveness was shown. However, local correlation of 2 binary features becomes 1 only when both features are 1. In other cases, local correlation becomes 0. This discarded information. In this paper, all kinds of co-occurrence of 2 binary features are used. This is the first device of our method. The second device is local Blob visual words. Conventional method made visual words from an orientation histogram on each grid. However, it is too local information. We use orientation histograms in a local Blob on grid as a basic feature and develop local Blob visual words. The third device is norm normalization of each orientation histogram in a local Blob. By normalizing local norm, the similarity between corresponding orientation histogram is reflected in subspace by KPCA. By these 3 devices, the accuracy is achieved more than 84% which is higher than conventional methods.