An automatic hierarchical image classification scheme
Jing Huang, S. Ravi Kumar, Ramin Zabih · 1998
Organizing images into semantic categories can be extremely useful for searching and browsing through large collections of images. Not much work has been done on automatic image classification, however. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages. 1 Introduction The proliferation of the world-wide web has given easy access to an explosively growing volume of visual data. Unfortunately, this data on the web is both scattered and unorganized, making search and retrieval...