Complex Cell Descriptor Learning for Robust Object Recognition

Zhe WANG  Yaping HUANG  Siwei LUO  Liang WANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.7   pp.1502-1505
Publication Date: 2011/07/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.1502
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
Independent Component Analysis,  overcomplete,  complex cell,  invariant feature,  object recognition,  Caltech-101,  

Full Text: PDF>>
Buy this Article

An unsupervised algorithm is proposed for learning overcomplete topographic representations of nature image. Our method is based on Independent Component Analysis (ICA) model due to its superiority on feature extraction, and overcomes the weakness of traditional method in fast overcomplete learning. Besides, the learnt topographic representation, resembling receptive fields of complex cells, can be used as descriptors to extract invariant features. Recognition experiments on Caltech-101 dataset confirm that these complex cell descriptors are not only efficient in feature extraction but achieve comparable performances to traditional descriptors.