Open AccessNon‐attention region first initialisation of k ‐means clustering for saliency detection
Author(s) -
Chen Jiazhong,
Cao Hua,
Ju Zengwei,
Qin Leihua,
Su Shuguang
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2013.2181
Subject(s) - cluster analysis , artificial intelligence , contrast (vision) , pattern recognition (psychology) , k means clustering , computer science , saliency map , cutoff , mathematics , image (mathematics) , computer vision , physics , quantum mechanics
According to the nature of saliency map generation with colour contrast, a non‐attention region first initialisation (NARFI) k ‐means clustering for saliency detection is proposed. The NAR is obtained by multiwavelet reconstruction based on the cutoff low‐frequency. The initial seeds of the k ‐means are chosen from the NAR. This way, the NAR is clustered in a fine manner, whereas the attention region is clustered in a coarse manner. As a result, the saliency values of the attention region with the NARFI k ‐means clustering are more conspicuous than those with the k ‐means++ clustering.