ICPR 2008 19th International Conference on Pattern Recognition
Download PDF

Abstract

Inspired by the success of inverted indexing in the textual search domain, we provide sparseness justifications for using inverted file indexing on image content, which paves the way for developing scalable image content search systems. We use clustering to automatically generate a content vocabulary. To avoid the problem of generating cluster centers that are overcrowded in high density areas for sparse data sets, we use a cluster-merge procedure for cluster post-processing. We further use visual codewords to represent low level image features, which not only makes the inverted file indexing and search applicable to image content, but also helps bridge the gap between the low level image features and high-level human visual perception. Experimental results confirm the success of our methods.
Like what you’re reading?
Already a member?Sign In
Member Price
$11
Non-Member Price
$21
Add to CartSign In
Get this article FREE with a new membership!

Related Articles