Performance Evaluation and Scheme Selection of Shot Boundary Detection and Keyframe Extraction in Content-Based Video Retrieval

Performance Evaluation and Scheme Selection of Shot Boundary Detection and Keyframe Extraction in Content-Based Video Retrieval

Lingchen Gu (Shandong University, School of Information Science and Engineering, Jinan, China), Ju Liu (Shandong University, School of Information Science and Engineering, Jinan, China) and Aixi Qu (Shandong University, School of Information Science and Engineering, Jinan, China)
Copyright: © 2017 |Pages: 15
DOI: 10.4018/IJDCF.2017100102
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Abstract

The advancement of multimedia technology has contributed to a large number of videos, so it is important to know how to retrieve information from video, especially for crime prevention and forensics. For the convenience of retrieving video data, content-based video retrieval (CBVR) has got great publicity. Aiming at improving the retrieval performance, we focus on the two key technologies: shot boundary detection and keyframe extraction. After being compared with pixel analysis and chi-square histogram, histogram-based method is chosen in this paper. Then we combine it with adaptive threshold method and use HSV color space to get the histogram. For keyframe extraction, four methods are analyzed and four evaluation criteria are summarized, both objective and subjective, so the opinion is finally given that different types of keyframe extraction methods can be used for varied types of videos. Then the retrieval can be based on keyframes, simplifying the process of video investigation, and helping criminal investigation personnel to improve work efficiency.
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Introduction

With the development of the Internet and multimedia information technology, videos become one of the main carriers of the modern information transmission. How to obtain user interest from a large number of videos efficiently has become an issue in the field of video retrieval (Yin et al. 2010; Yang & Meinel 2014). The traditional approach of video retrieval is based on the technology of database management systems, with the cost of heavy burden of manual annotation. It is time-consuming, and the manual tag of video information may be inaccurate sometimes. In order to solve these problems, content-based video retrieval (CBVR) was proposed (Hoi & Lyu 2008; Hu et al. 2011).

“Content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself (Patel et al. 2012; Dyana et al. 2010). CBVR is based on the effective analysis of scene, shot and frame in the video data, to extract the video features, such as pixel (Ling et al. 2008), color (Guo et al. 2016), edge (Priya et al. 2012) and motion (Chen & Wu 2011) of low-level features and semantics (Agharwal et al. 2016) of high-level features. To get video with the highest matching similarity and meet users’ satisfaction, we need to compare the features between user input and large-scale database (Wang et al. 2015).

Video stream has a great number of data and it is non-structured. Therefore, it is difficult for us to use the whole video to retrieve directly. In order to achieve effective structural analysis, we do shot boundary detection (Lu & Shi 2013), and then keyframe extraction (Yin et al. 2010). Thus, shot boundary detection and keyframe extraction are very important in CBVR technologies. Applied to forensics, video retrieval technology can help criminal investigation personnel to focus on information which they need efficiently and accurately.

Shot boundary detection is necessary for almost all video analysis, indexing, search, browsing and content-based operations (Smeaton et al. 2010), with most research work focused on it. Then keyframe extraction is carried out after shot segmentation. The keyframe is a frame or a number of frames which reflect the main content of shots or scenes. The content must be as representative as possible (Chao et al. 2010). Therefore, the use of keyframe greatly reduces the amount of data required in the video retrieval and browsing, and provides a framework for organizing the processing of video content (Chakraborty et al. 2015). Then we can retrieve the required information through the keyframe quickly, so as to improve the efficiency. In particular, it can save a lot of valuable time to use the keyframe to retrieve in the criminal investigation.

The rest of this paper is organized as follows. Section 2 introduces three common methods of shot boundary detection. After comparison, the method used in this paper is obtained. And different methods are tested and analyzed by experiments. Section 3 describes four keyframe extraction methods and four evaluation standards. Through experimental analysis, we summarize the keyframe extraction methods under different evaluation criteria, and finally put forward the different types of videos can adopt different types of keyframe extraction method. Section 4 concludes the work.

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