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EURASIP Journal on Advances in Signal Processing

, 2011:121

First Online: 05 December 2011Received: 15 April 2011Accepted: 05 December 2011

Abstract

In this article, a method for recommendation of music pieces according to human motions based on their kernel canonical correlation analysis CCA-based relationship is proposed. In order to perform the recommendation between different types of multimedia data, i.e., recommendation of music pieces from human motions, the proposed method tries to estimate their relationship. Specifically, the correlation based on kernel CCA is calculated as the relationship in our method. Since human motions and music pieces have various time lengths, it is necessary to calculate the correlation between time series having different lengths. Therefore, new kernel functions for human motions and music pieces, which can provide similarities between data that have different time lengths, are introduced into the calculation of the kernel CCA-based correlation. This approach effectively provides a solution to the conventional problem of not being able to calculate the correlation from multimedia data that have various time lengths. Therefore, the proposed method can perform accurate recommendation of best matched music pieces according to a target human motion from the obtained correlation. Experimental results are shown to verify the performance of the proposed method.

Keywordscontent-based multimedia recommendation kernel canonical correlation analysis longest common subsequence p-spectrum AbbreviationsCCAcanonical correlation analysis

MMDmultimedia documents

LCSSlongest common subsequence

LCSS-KLCSS: kernel

SI-Kspectrum intersection kernel

G-Kgaussian kernel

S-Ksigmoid kernel

L-Klinear kernel.

Electronic supplementary materialThe online version of this article doi:10.1186-1687-6180-2011-121 contains supplementary material, which is available to authorized users.

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Author: Hiroyuki Ohkushi - Takahiro Ogawa - Miki Haseyama

Source: https://link.springer.com/







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