Fast Video Search Algorithm for Large Video Database Using Adjacent Pixel Intensity Difference Quantization Histogram Feature

In this paper, we present a fast and robust video search algorithm for large video database using the histogram feature which is essentially different from conventional ones. This algorithm is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of a frame image. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be achieved. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 video clips which having a each length of 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 80ms, and is more accurate and robust against Gaussian noise than conventional fast video search algorithm.