Open AccessImage retrieval based on histograms of EOPs and VQ indices
Author(s) -
Lu ZheMing,
Feng YaPei
Publication year - 2016
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.2016.2596
Subject(s) - pattern recognition (psychology) , histogram , artificial intelligence , block (permutation group theory) , cluster analysis , feature (linguistics) , image retrieval , mathematics , orientation (vector space) , image (mathematics) , precision and recall , computer science , computer vision , linguistics , philosophy , geometry
A new feature for image retrieval based on edge orientation patterns (EOPs) by classifying image blocks into nine classes according to their EOs. Each input colour image is decomposed into Y, Cb and Cr components, and each component image is divided into non‐overlapping 4 × 4 blocks. For each block, eight EO templates of size 4 × 4 are performed to determine its EO. On the other hand, vector quantisation (VQ) is an efficient clustering and classification technique for high‐dimensional spaces, and thus VQ is also used to extract the texture features. The retrieval simulation results show that the EOP‐histogram‐based features combined with the traditional VQ‐index‐histogram‐based features can largely improve the recall and precision performance.