Color Independent Components Based SIFT Descriptors for Object/Scene Classification

Dan-ni AI  Xian-hua HAN  Xiang RUAN  Yen-wei CHEN  

IEICE TRANSACTIONS on Information and Systems   Vol.E93-D   No.9   pp.2577-2586
Publication Date: 2010/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.2577
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
CIC-SIFT descriptor,  object/scene classification,  ICA-based transformation,  

Full Text: PDF>>
Buy this Article

In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.