Compressing histogram representations for automatic colour photo categorization
Organizing images into semantic categories can be very useful for searching and browsing through large image repositories. In this work, we use machine learning to associate low level colour representations of digital colour photos with their high level semantic categories. We investigate the redundancy and performance of a number of histogram-based colour image content representations in the context of automatic colour photo categorization using support vector machines. We use principal component analysis to reduce the dimensionality of (high dimensional) histogram based colour descriptors and use support vector machines to learn to classify the images into various high level categories in the histograms subspaces. We present experimental results to demonstrate the usefulness of such an approach to organizing colour photos into semantic categories. Our results show that the colour content descriptors constructed in different ways perform quite differently and the performances are data dependent hence it is difficult to pick a winning descriptor. Our results demonstrate conclusively that all descriptors studied in this paper are highly redundant and that regardless of their performances, the dimensionalities of these histogram based colour content descriptors can be significantly reduced without affecting their classification performances.
color histogram content-based indexing and retrieval pca svm