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Proceedings Paper

An image feature data compressing method based on product RSOM
Author(s): Jianming Wang; Lihua Liu; Shengping Xia
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Paper Abstract

Data explosion and information redundancy are the main characteristics of the era of big data. Digging out valuable information from mass data is the premise of efficient information processing, which is a key technology in the area of object recognition with mass feature database. In the area of large scale image processing, both of the massive image data and the image features of high-dimension take great challenges to object recognition and information retrieval. Similar with big data, the large scale image feature database, which contains extensive quantity of information redundancy, can also be quantitatively represented by finite clustering models without degrading recognition performance. Inspired by the ideas of product quantization and high dimensional feature division, a data compression method based on recursive self-organizing mapping (RSOM) algorithm is proposed in this paper.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130D (14 December 2015); doi: 10.1117/12.2205833
Show Author Affiliations
Jianming Wang, National Univ. of Defense Technology (China)
Lihua Liu, National Univ. of Defense Technology (China)
Shengping Xia, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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