Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, JH. et al. (2006). Hierarchical Classification of Object Images Using Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_47
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DOI: https://doi.org/10.1007/11760023_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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