Burst-survive Temporal Matching Kernel with Fibonacci Periods

Abstract

In this paper we present a novel approach to improve temporal matching kernel (TMK) for video retrieval tasks. TMK has the ability to align videos during retrieval, but provides little to none retrieval performance improvement over baseline methods. We discovered that TMK cannot discriminate between a true match case in which two videos have long, consecutive segments of similar frames and a false match case in which two videos contain non-consecutive segments of randomly similar frames. Our proposed burst-survive temporal matching kernel adopts a novel shuffle strategy to rule out false match cases, with the assistance of multiple periods selected from Fibonacci series. As a result, we achieved significant performance improvement on the EVVE dataset.