Content-based image retrieval using local texture-based color histogram

Abstract

this paper presents a novel image feature representation method, called local texture-based color histogram (LTCH), for content-based image retrieval. The LTCH can describe the color distribution under a mask, which is defined as a micro-structure image with a near-uniform texture. The near-uniform texture is exacted by center symmetric local trinary pattern (CS-LTP) and micro-structure map. The CS-LTP is coding on a quantized HSV image, and the micro-structure map is defined with the same as CS-LTP code. The LTCH can be considered as a novel visual attribute descriptor combining local texture, color and spatial layout, without any image segmentation and model training. The proposed LTCH method is evaluated on Corel-1000 database and Corel-5000 database with the standard performance evaluation method, for image retrieval. The experimental results demonstrate that the proposed method has a better performance than representative image feature descriptors, such as color difference histogram (CDH), microstructure descriptor (MSD), multi-texton histogram (MTH) and structure elements' descriptor (SED). Keywords-near-uniform texture; center symmetric local trinary pattern; micro-structure map; color histogram; contentbased image retrievalI.978-1-4799-8322-3/15/$31.00 ©2015 IEEE

Color and cross diagonal symmetric pair co-occurrence matrix

“…Where N ic is the number of i th category images in the database, 'n' is the number of retrieved images and |DB| indicates the size of image database. The performance efficacy of the proposed CCDSP-CM and CICDSP-CM descriptors are judged against the existing descriptors: local binary pattern (LBP) (7) , semi-structure local binary pattern (SLBP) (8) , local directional pattern (LDP) (13) , local ternary pattern (LTP) (9) , center symmetric LBP (CS-LBP) (40) , block based LBP (BLK-LBP) (48) using APR and ARR on the databases considered and the results are shown in the graphs in Figures 11,12,13,14,15,16,17,18,19 1. The proposed framework CCDSP-CM exhibited high retrieval rate and discrimination abilities on Corel-1000, Corel-10000, CBT and MIT-visTex databases with an average precision as 84.69%, 86.32%, 87.24% and 85.12% respectively during the retrieval of top 10 images.…”

“…The retrieval performance of color descriptors is generally limited because of inadequate discrimination capability. In recent past, researchers achieved better results in image retrieval by integrating color and texture features of the image (18)(19)(20) . Z. Zeng (21) proposed local structure descriptor (LSD) for color image retrieval.…”

Correlated Microstructure Descriptor for Image Retrieval

“…Edge orientation with underlying colors and spatial layout are effectively combined to represent image features by microstructures [35]. Local Texture-based Color Histogram (LTCH) effectively combines color and texture [36]. Color distribution is defined by microstructure image and Local Trinary Pattern (LTP) extract the textural features.…”

“…Both color and texture features fused together to represent a single feature set [52]. The proposed approach in [36] describes various shape-based descriptors in CBIR domain like invariant moments, edge curvature and arc length, polygonal approximation, Fourier transform coefficient, curvature scale space and Edge Orientation Auto Correlograms (EoAC).…”