Image Database Design Based on 9D-SPA Representation for Spatial Relations
IEEE Transactions on Knowledge and Data Engineering
Image information mining system evaluation using information-theoretic measures
EURASIP Journal on Applied Signal Processing
Mutual information based measure for image content characterization
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
| Hi-index | 0.00 |
We present a framework for measuring the complexity of image databases, which characterizes the databases for image retrieval. Motivated from the concept of text corpus perplexity, the complexity of image databases is formulated based on image database statistics and information theory. We propose a quantitative metric which can be used to measure the degree of difficulty to retrieve images based on contents of the database. This metric is independent of queries, hence, it is objective. Experiments on both synthetic and real-world images demonstrate that the proposed measurement is highly effective in quantitatively measuring the contents of image databases for content-based retrieval.