A systematic review on content-based video retrieval. (April 2020)

• Record Type: Journal Article
• Authors: Spolaôr, Newton; Lee, Huei Diana; Takaki, Weber Shoity Resende; Ensina, Leandro Augusto; Coy, Claudio Saddy Rodrigues; Wu, Feng Chung
• Is Part Of: Engineering applications of artificial intelligence. Volume 90(2020)
• Publication Date: 2020-04
• Subjects: Color features -- Unsupervised learning -- Shot boundary detection; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103557
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Content-based video retrieval and indexing have been associated with intelligent methods in many applications such as education, medicine and agriculture. However, an extensive and replicable review of the recent literature is missing. Moreover, relevant topics that can support video retrieval, such as dimensionality reduction, have not been surveyed. This work designs and conducts a systematic review to find papers able to answer the following research question: "what segmentation, feature extraction, dimensionality reduction and machine learning approaches have been applied for content-based video indexing and retrieval?". By applying a research protocol proposed by us, 153 papers published from 2011 to 2018 were selected. As a result, it was found that strategies for cut-based segmentation, color-based indexing, k-means based dimensionality reduction and data clustering have been the most frequent choices in recent papers. All the information extracted from these papers can be found in a publicly available spreadsheet. This work also indicates additional findings and future research directions.