论文信息 - Minimum entropy and information measure

Minimum entropy and information measure

Kapur et al. (1995) introduced the MinMax information measure, which is based on both maximum and minimum entropy. The major obstacle for using this measure, in practice, is the difficulty in finding the minimum entropy. An analytical expression has already been developed for calculating the minimum entropy when only variance is specified. An analytical formula is obtained for calculating the minimum entropy when only mean is specified, and numerical examples are given for illustration.

Lin Yuan | H. K. Kesavan | H. Kesavan | Lin Yuan

[1] George Baciu,et al. The MinMax information measure , 1995 .

[2] Ronald J. Williams,et al. Incremental Multi-Step , 1996 .

[3] Satosi Watanabe,et al. Pattern recognition as a quest for minimum entropy , 1981, Pattern Recognit..

[4] Keith Price,et al. Picture Segmentation Using a Recursive Region Splitting Method , 1998 .

[5] J. N. Kapur. Maximum-entropy models in science and engineering , 1992 .

[6] Julian R. Ullmann. Edge replacement in the recognition of occluded objects , 1993, Pattern Recognit..

[7] Bir Bhanu,et al. Recognition of occluded objects: A cluster-structure algorithm , 1987, Pattern Recognit..

[8] E. Jaynes. Information Theory and Statistical Mechanics , 1957 .