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Human-Centered Video Feature Selection via mRMR-SCMMCCA for Preference Extraction
Takahiro OGAWA Yoshiaki YAMAGUCHI Satoshi ASAMIZU Miki HASEYAMA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2017/02/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Kansei Information Processing, Affective Information Processing
Canonical Correlation Analysis, feature selection, preference extraction, viewing behavior,
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This paper presents human-centered video feature selection via mRMR-SCMMCCA (minimum Redundancy and Maximum Relevance-Specific Correlation Maximization Multiset Canonical Correlation Analysis) algorithm for preference extraction. The proposed method derives SCMMCCA, which simultaneously maximizes two kinds of correlations, correlation between video features and users' viewing behavior features and correlation between video features and their corresponding rating scores. By monitoring the derived correlations, the selection of the optimal video features that represent users' individual preference becomes feasible.