Abstract
This work presents a novel shot boundary detection (SBD) method based on the Place-centric deep network (PlaceNet), with the aim of using video shots and image queries for video searching (VS) and fingerprint detection. The SBD method has three stages. In the first stage, we employed Local Binary Pattern-Singular Value Decomposition (LBP-SVD) features for candidate shot boundaries selection. In the second stage, we used the PlaceNet to select the shot boundary by semantic labels. In the third stage, we used the Scale-Invariant Feature Transform (SIFT) descriptor to eliminate falsely detected boundaries. The experimental results show that our SBD method is effective on a series of SBD datasets. In addition, video searching experiments are conducted by using one query image instead of video sequences. The results under several image transitions by using shot fingerprints have shown good precision.
Highlights
With videos becoming more popular, important, and pervasive, video tasks, such as searching, retrieving, tracking, summarization, object detection, and copy detection, are becoming more challenging
Transformations image queries from the source videos without transformations are taken for the experiments
We have proposed a new video searching and fingerprint detection method by detection, we tested several features and studied the thresholds
Summary
With videos becoming more popular, important, and pervasive, video tasks, such as searching, retrieving, tracking, summarization, object detection, and copy detection, are becoming more challenging. Video searching (VS) has been a challenging research topic since the mid-1990s, and video copy detection (VCD) started at that time [1]. Video fingerprinting is widely employed in VS and VCD. The tendency of VCD has been focused on the extraction of robust fingerprints. The state-of-the-art VCD methods are mostly based on video sequences, and image-query-based. Developing robust fingerprints for VS/VCD by using image queries has great importance
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