An Effective Local Invariant Descriptor Combining Luminance and Color Information

Extraction of stable local invariant features is very important in many computer vision applications, such as image matching, object recognition and image retrieval. Most existing local invariant features mainly characterize luminance information, and neglect color information. In this paper, we present a new local invariant descriptor characterizing both of them, which combines three photometric invariant color descriptors with the famous SIFT descriptor. To reduce the dimension of the combined high-dimensional invariant feature the principal component analysis (PCA) is used. Our experiments show the proposed local descriptor through combining luminance and color information outperforms the descriptors that only utilize a single category of information, and combining the three color feature representations is more effective than only one.

• Type: Conference
• Year: 2007
• Where: ICMCS
• Authors: Dong Zhang, Weiqiang Wang, Wen Gao, Shuqiang Jiang
• Keywords: ICMCS 2007, Invariant Feature, Local Invariant Descriptor, Local Invariant Features