Feature selection in multimedia: The state-of-the-art review

被引：24
作者：
Lee, Pui Yi [1 ]
Loh, Wei Ping [1 ]
Chin, Jeng Feng [1 ]
机构：
[1] Univ Sains Malaysia, Sch Mech Engn, Engn Campus, Nibong Tebal 14300, Penang, Malaysia
来源：
IMAGE AND VISION COMPUTING | 2017年 / 67卷
关键词：
Feature selection; Multimedia; Data mining; Search strategies; FEATURE SUBSET-SELECTION; SPARSE FEATURE-SELECTION; TEXT DETECTION; INSTANCE SELECTION; MUTUAL INFORMATION; IMAGE ANNOTATION; ATTENTION MODEL; VIDEO; CLASSIFICATION; RECOGNITION;
D O I：
10.1016/j.imavis.2017.09.004
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Multimedia data mining, particularly feature selection (FS), has been successfully applied in recent classification and recognition works. However, only a few studies in the contemporary literature have reviewed FS (e.g., analyses of data pre-processing prior to classification and clustering). This study aimed to fill this research gap by presenting an extensive survey on the current development of FS in multimedia. A total of 70 related papers published from 2001 to 2017 were collected from multiple databases. Breakdowns and analyses were performed on data types, methods, search strategies, performance measures, and challenges. The development trend of FS presages the increased prominence of heuristic search strategies and hybrid FS in the latest multimedia data mining. (C) 2017 Published by Elsevier B.V.
引用
收藏
页码：29 / 42
页数：14
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