Efficient large scale near-duplicate video detection base on spark

Abstract

With the huge amount of web video data and its exponential growth in recent years, there are new challenges in Near-Duplicate Video Detection (NDVD) which have attracted much attention owing to its wide applications. One of the problems is how to extract discriminative features to achieve higher precision, and the other problem is how to improve the efficiency of large scale video analysis. Existing methods have predominantly focused on improving the precision and achieve some success on NDVD. However, it is an extremely challenging task to guarantee the higher precision and efficiency simultaneously. To balance the efficiency and precision, we propose a Multi-Feature based Parallel System (MFPS) for large scale NDVD to overcome these challenges. For feature extraction, we combine local and global features to accurately represent the critical information of video. In our work, not only Scale-Invariant Feature Transform (SIFT) but also Local Maximal Occurrence (LOMO) is introduced as local features and Color Name (CN) is adopted as global feature. Meanwhile, for SIFT and CN features, Bag-of-Visual-Words (BoVW) method is used to quantize the features due to its accuracy and efficiency in NDVD. Furthermore, we design a similarity measure for video pairs which is inspired by hough transform and sliding window idea. Finally, we implement the system on parallel cloud computing platform based on spark. A comprehensive evaluation is conducted on CC_WEB_VIDEO dataset which includes 12790 videos and 27% near-duplicates. Experimental results show that the proposed method is able to achieve some improvements in term of mAP against other methods. Moreover, our approach is efficient and achieves 5.8X speedup.