Distributional clustering for efficient content-based retrieval of images and video
- 11 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 81-84 vol.1
- https://doi.org/10.1109/icip.2000.900897
Abstract
We present an approach to clustering images for efficient retrieval using relative entropy. We start with the assumption that visual features are represented by probability densities and develop clustering algorithms for probability densities (for example, normalized histograms are crude approximations of probability densities). These clustering algorithms are then used for efficient retrieval of images and video.Keywords
This publication has 7 references indexed in Scilit:
- VideoBook: an experiment in characterization of videoPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A Bayesian framework for semantic content characterizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Content analysis of video using principal componentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Content-based search of video using color, texture, and motionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Photobook: Content-based manipulation of image databasesInternational Journal of Computer Vision, 1996
- Query by image and video content: the QBIC systemComputer, 1995
- Vector quantization by deterministic annealingIEEE Transactions on Information Theory, 1992