https://scholars.lib.ntu.edu.tw/handle/123456789/413004
標題: | Facial attribute space compression by latent human topic discovery | 作者: | Lin C.-H. Chen Y.-Y. Chen B.-C. Hou Y.-L. WINSTON HSU |
關鍵字: | Attribute correlation; Compression; Facial attribute detection | 公開日期: | 2014 | 起(迄)頁: | 1157-1160 | 來源出版物: | 2014 ACM Multimedia Conference | 摘要: | Facial attribute is important information for a variety of machine vision tasks including recognition, classification, and retrieval. There arises a strong need for detecting various facial attributes such as gender, age and more which consume more computation and storage resources. Therefore, we propose a compression framework to find fewer significant Latent Human Topics (LHT) to approximate more facial attributes. LHT is a combination of attribute correlation by transferring facial attribute space to compressional space with Singular Value Decomposition (SVD). Using the proposed scheme, we can easily detect the facial attributes from a face image via fast reconstructing the compressed labels automatically detected by a few LHT classifiers. Experimental results show that our system can achieve similar performance with substantially fewer dimensions compared to the original number of facial attributes, and it even shows slight improvements because LHT carry informative attribute correlations learned from data. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413004 | ISBN: | 9781450330633 | DOI: | 10.1145/2647868.2655016 |
顯示於: | 資訊工程學系 |
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