FUZZY MODE SIMILARITY MEASURE BASED ON MEASURE OF ILLUMINATION INSTABILITY
Xiaomeng Wu, Wenli Zhang, Shunsuke Kamijo, Masao Sakauchi · International Journal of Semantic Computing · 2008
Most color-based multimedia data indexing systems are sensitive to changes in the illumination environment. Some color-based systems coming into practical use are claimed to be limited to target videos with stable lighting. In this paper, a fuzzy mode similarity measure is proposed to adaptively calibrate the feature-matching criterion based on the measure of the illumination instability. The purpose of this approach is to retrieve the identical semantic object from the video and to alleviate the impact of lighting changes. An information-theoretic measure is first proposed to automatically measure the illumination instability of the video. A fuzzy model is then proposed to estimate and characterize the impact of illumination changes to the distribution shape of the semantic object in the low-level visual feature space. Experiments are shown to demonstrate how the proposed measure of the illumination instability, which takes the information distribution within an image into account, can reflect the instability more effectively than other simple and straightforward measures. Experiments also show how the retrieval performance can be improved by using the fuzzy mode similarity measure.