Semantic Video Clustering in Ad Hoc Networks for Content-Based Retrieval

Traditional content-based retrieval approaches employ either centralized or flooding strategies in ad hoc networks, which may result in low fault tolerance and high search cost making them inefficient. To facilitate an efficient video retrieval, we propose a logic-based content summary framework that is able to represent semantic contents of video data using concise logic terms. In this method the video data is characterized by color and wavelet coefficients which will be converted into logical terms by using threshold operators. The logical terms are then summarized as node content descriptions. The nodes containing similar node descriptions are clustered into a virtual infrastructure according to the semantic content.