Hierarchical classification of object images using neural networks

• Authors: Jong-Ho Kim;Jae-Won Lee;Byoung-Doo Kang;O-Hwa Kwon;Chi-Young Seong;Sang-Kyoon Kim;Se-Myung Park
• Venue: ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
• Year: 2006

We propose a hierarchical classifier of object images using neural networks for content-based image classification. The images for classification are object images that can be divided into foreground and background areas. In the preprocessing step, we extract the object region and shape-based texture features extracted from wavelet-transformed images. We group the image classes into clusters that have similar texture features using Principal Component Analysis (PCA) and K-means. The hierarchical classifier has five layers that combine the clusters. The hierarchical classifier consists of 59 neural network classifiers that were learned using the back-propagation algorithm. Of the various texture features, the diagonal moment was the most effective. A test showed classification rates of 81.5% correct with 1000 training images and of 75.1% correct with 1000 test images. The training and test sets each contained 10 images from each of 100 classes.