Circulant temporal encoding for video retrieval and temporal alignment

We address the problem of specific video event retrieval. Given a query video of a specific event, e.g., a concert of Madonna, the goal is to retrieve other videos of the same event that temporally overlap with the query. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to efficiently compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. The descriptors can be compressed in the frequency domain with a product quantizer adapted to complex numbers. In this case, video retrieval is performed without decompressing the descriptors. We also consider the temporal alignment of a set of videos. We exploit the matching confidence and an estimate of the temporal offset computed for all pairs of videos by our retrieval approach. Our robust algorithm aligns the videos on a global timeline by maximizing the set of temporally consistent matches. The global temporal alignment enables synchronous playback of the videos of a given scene

L1-norm global geometric consistency for partial-duplicate image retrieval

In all feature point based partial-duplicate image retrieval systems, false matching is a common issue. To tackle the problem, geometric contexts are widely applied to filter the inconsistent matches. This paper presents a novel method called 1-norm global geometric consistency. We first form the squared distance matrices of all the matched feature points, which remain invariant under translation and rotation between partial-duplicated images. Then we find the scale difference by solving a one-variable 1-norm error minimization problem, where the large sparse errors correspond to the locations of inconsistent matches. By adopting the Golden Section Search method the minimization problem can be solved efficiently. Extensive experimental results show that our method reaches higher precisions than state-of-the-art geometric verification methods in detecting inconsistent matches. Its speed is also highly competitive even when compared to local geometric consistency based methods. ? 2014 IEEE.EI3033-303