Improved content-based image classification using a random forest classifier

Abstract

Content-based image classification is being one of the important phase in the process of automatic retrieval and annotation of images. In this paper, we are focused on effective image classification using low-level colour and texture features. It is well proved that the classifier performances are good for the unknown images, if quite similar images are present in the training set. On the other hand, classifier performances could not be guaranteed for images that are very much dissimilar from training set. This generalization problem of classifier can bias the image retrieval and annotation process. This paper objective is to investigate the discrimination abilities for such different class standard images. For improved image classification, we extract the effective low-level colour and texture features from the images. These features include; local binary pattern (LBP) based texture features, and colour percentile, colour moment, and colour histogram based colour features. To overcome the generalization problem, we have used random forest classifier, capable for handle over-fitting situation. Experimental analysis on benchmark database confirms the effectiveness of this work. © Springer Nature Singapore Pte Ltd. 2018.