DocumentCode :
588894
Title :
Scene Recognition via Combining Information of Neighbors
Author :
Minguang Song ; Ping Guo
Author_Institution :
Image Process. & Pattern Recognition Lab., Beijing Normal Univ., Beijing, China
fYear :
2012
fDate :
17-18 Nov. 2012
Firstpage :
345
Lastpage :
349
Abstract :
Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. When combining PACT with Local difference Magnitude Binary Pattern (LMBP), a new representation called Local Difference Binary Pattern (LDBP) was proposed and performed better. LDBP is based on the comparisons between center pixel and its neighboring pixels. However, the relationship among neighbor pixels is not considered. In this paper we proposed Local Neighbor Binary Pattern (LNBP) to utilize the relationship among neighboring pixels. LNBP provides complementary information regarding neighboring pixels for LDBP. We propose to combine LDBP with LNBP, and used a spatial representation for scene recognition. Experiments on two widely used dataset demonstrate the proposed method can improve the performance of recognition.
Keywords :
image recognition; image representation; natural scenes; principal component analysis; transforms; LDBP; LMBP; PACT; center pixel; complementary information; local difference binary pattern; local difference magnitude binary pattern; neighboring pixels; place category recognition; place instance recognition; scene category recognition; scene instance recognition; scene recognition performance improvement; spatial principal component analysis-of-census transform histograms; spatial representation; Computer vision; Conferences; Histograms; Image recognition; Pattern recognition; Principal component analysis; Transforms; local binary pattern; scene recognition; spatial pyramid matching;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security (CIS), 2012 Eighth International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4673-4725-9
Type :
conf
DOI :
10.1109/CIS.2012.84
Filename :
6405942
Link To Document :
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