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1. (WO2018103179) NEAR-DUPLICATE IMAGE DETECTION METHOD BASED ON SPARSE REPRESENTATION

Pub. No.:    WO/2018/103179    International Application No.:    PCT/CN2017/070197
Publication Date: Fri Jun 15 01:59:59 CEST 2018 International Filing Date: Fri Jan 06 00:59:59 CET 2017
IPC: G06K 9/62
Applicants: NORTHWESTERN UNIVERSITY
西北大学
Inventors: ZHAO, Wanqing
赵万青
LUO, Hangzai
罗迒哉
FAN, Jianping
范建平
PENG, Jinye
彭进业
Title: NEAR-DUPLICATE IMAGE DETECTION METHOD BASED ON SPARSE REPRESENTATION
Abstract:
A near-duplicate image detection method based on sparse representation. The method is proposed on the basis of a Hadoop distributed computing framework, and comprises the following steps: acquiring an image set I, wherein sparse encoding results of all images are g'; extracting non-zero elements in g', and hashing the sparse encoding result gi' of the image Ii to groups corresponding to subscripts of the non-zero elements; computing, for each reduce function, a similarity level Y of the sparse encoding results for each pair of images , and if Y is greater than 0.7, then outputting said near-duplicate image pair ; and combining near-duplicate image pairs having the image Iw, and generating a near-duplicate image subset. The technical solution of the present invention employs parallel computing to greatly improve computational efficiency of a K-Means clustering algorithm for a large-scale data set, and introduces the sparse representation concept to increase the speed of the method and eliminate excessive computation for solution finding and optimization.