Open AccessImage Description and Matching Scheme Using Synthetic Features for Recommendation Service
Author(s) -
Yang WonKeun,
Cho Ayoung,
Oh WeonGeun,
Jeong DongSeok
Publication year - 2011
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.11.1510.0023
Subject(s) - computer science , histogram , robustness (evolution) , pattern recognition (psychology) , artificial intelligence , matching (statistics) , feature extraction , partition (number theory) , feature (linguistics) , image (mathematics) , data mining , computer vision , mathematics , statistics , biochemistry , chemistry , linguistics , philosophy , gene , combinatorics
This paper presents an image description and matching scheme using synthetic features for a recommendation service. The recommendation service is an example of smart search because it offers something before a user's request. In the proposed extraction scheme, an image is described by synthesized spatial and statistical features. The spatial feature is designed to increase the discriminability by reflecting delicate variations. The statistical feature is designed to increase the robustness by absorbing small variations. For extracting spatial features, we partition the image into concentric circles and extract four characteristics using a spatial relation. To extract statistical features, we adapt three transforms into the image and compose a 3D histogram as the final statistical feature. The matching schemes are designed hierarchically using the proposed spatial and statistical features. The result shows that each feature is better than the compared algorithms that use spatial or statistical features. Additionally, if we adapt the proposed whole extraction and matching scheme, the overall performance will become 98.44% in terms of the correct search ratio.