Title :
Class-specific codebook construction for biologically inspired recognition
Author :
Gao, Jun ; Gao, Changxin ; Sang, Nong ; Tang, Qiling
Author_Institution :
Inst. for Pattern Recognition & Artificial Intell., Huazhong Univ. of Sci. & Technol., Wuhan
Abstract :
A novel method is presented to improve the object recognition performance of a biologically inspired model by learning class-specific feature codebook. The feature codebook is multi-class shared in the original model, and the content proportion for different codeword type is set in uniform distribution. According to corresponding discriminability, the codebook content proportion is adjusted upon different codeword types (feature vector sizes and filter scales). The test results demonstrate that the codebooks built with proposed modification achieve higher total-length efficiency.
Keywords :
feature extraction; image classification; image reconstruction; medical image processing; object recognition; biologically inspired recognition; class-specific codebook construction; class-specific feature codebook; feature codebook; feature vector sizes; object recognition performance; uniform distribution; Artificial intelligence; Biological information theory; Biological system modeling; Biology computing; Dictionaries; Filters; Object recognition; Pattern recognition; Proposals; Prototypes; Classification efficiency; Computation model; Discriminability distribution; Feature codebook; Object recognition; Pattern recognition;
Conference_Titel :
Advance Computing Conference, 2009. IACC 2009. IEEE International
Conference_Location :
Patiala
Print_ISBN :
978-1-4244-2927-1
Electronic_ISBN :
978-1-4244-2928-8
DOI :
10.1109/IADCC.2009.4809073