Scene categorization with multi-scale category-specific visual words

Abstract

In this paper, we propose a scene categorization method based on multi-scale category-specific visual words. The proposed method quantizes visual words in a multi-scale manner which combines the global-feature-based and local-feature-based scene categorization approaches into a uniform framework. Unlike traditional visual word creation methods which quantize visual words from the whole training images without considering their categories, we form visual words from the training images grouped in different categories then collate the visual words from different categories to form the final codebook. This category-specific strategy provides us with more discriminative visual words for scene categorization. Based on the codebook, we compile a feature vector that encodes the presence of different visual words to represent a given image. A SVM classifier with linear kernel is then employed to select the features and classify the images. The proposed method is evaluated over two scene classification datasets of 6,447 images altogether using 10-fold cross-validation. The results show that the classification accuracy has been improved significantly comparing with the methods using the traditional visual words. And the proposed method is comparable to the best results published in the previous literatures in terms of classification accuracy rate and has the advantage in terms of simplicity. © 2009 SPIE-IS&T.