Chaos-Synchronization Based Representation of Objects and Events From MPEG-7 Low-Level Descriptors

Chaos theory has been reported to simulate partial functions (i.e., neuronal activity in brain) of the human visual system. In this work, we propose a chaotic synchronization-based representation of semantic entities (objects, events) in surveillance scenes using MPEG-7 low level Ds. MPEG-7 visual descriptors are used to extract low-level features of video objects. The chaotic synchronization is used to perform feature binding (i.e., group semantically relevant feature elements) from these Ds. The objective is to search for unique numeric descriptions (based on low-level features) to identify semantic entities. The idea of a semantic space is introduced to explain feature binding from multiple feature spaces. Subjective evaluation (based on classification) shows the existence of such numeric description for related semantic entities (e.g., male, female, automobile, multiple persons, enter appear, move)