Image retrieval by local contrast patterns and color

Authors:
M. K. Bashar;N. Ohnishi
Affiliations:
Graduate School of Information Science, Nagoya University, Nagoya, Aichi, Japan;Graduate School of Information Science, Nagoya University, Nagoya, Aichi, Japan
Venue:
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Year:
2006

Citing 9
Cited 0

Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval Experiment

Information Retrieval Experiment
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Indexing Using Color Correlograms

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Wavelet transform-based locally orderless images for texture segmentation

Pattern Recognition Letters
Image retrieval by pattern categorization using wavelet domain perceptual features with LVQ neural network

Pattern Recognition Letters
Natural basis functions and topographic memory for face recognition

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Block-Based methods for image retrieval using local binary patterns

SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Color and texture descriptors

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Despite simplicity of the Local binary patterns (LBP) or local edge patterns (LEP) for texture description, they do not always convey complex pattern information. Moreover they are susceptive to various image distortions. Hence we propose a new descriptor called Local Contrast Patterns(LCP), which encode the joint difference distribution of filter responses that can be effectively computed by the higher order directional Gaussian derivatives. Though statistical moments of the filter responses are typical texture features, various complex patterns ( e.g., edges, points, blobs) are well captured by the proposed LCP. Observation shows that anyone of the first few derivatives can produce promising results compared to LBP(or LEP). To extract more improved outcome, two sub-optimal descriptors (LCP1, LCP2) are computed by maximizing local bit frequency and local contrast-ratio. Global RGB color histogram is then combined with the proposed LCP descriptors for color-texture retrieval. Experiments with the grayscale (Brodatz album) and color-texture (MIT VisTex) databases show that our proposed LCP (LCP+RGB) produces 8 % and 2.1 % (1.4 % and 1.9 % ) improved recall rates compared to LBP and LEP (LBP+RGB and LEP+RGB) features. The achievement of the lowest rank ratio, i.e., 2.789 for gray images (1.482 for color images) also indicates the potentiality of the proposed LCP2(LCP2+RGB) feature.