Image retrieval based on multi-texton histogram

Liu, Guanghai; Zhang, Lei; Hou, Yingkun; Li, Zuoyong; Yang, Jingyu

doi:10.1016/j.patcog.2010.02.012

Cited by 266 publications

(213 citation statements)

References 37 publications

Supporting

Mentioning

203

Contrasting

Unclassified

Order By: Relevance

“…For a fair evaluation of MSD's performance in image retrieval, we selected those algorithms which are specifically developed for image retrieval, such as Gabor features [3] and multi-textons histogram (MTH) [4], in the experiments for comparison. In the experiments, we selected randomly 50 images from each category as query images.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, the MSD algorithm simulates human visual perception mechanism to some extent. Our experiments on large-scale datasets show that the MSD achieves higher retrieval precision than representative texture feature descriptors, such as Gabor feature [3] and our previous work called multi-textons histogram (MTH) [4], for image retrieval.…”

Section: Introductionmentioning

confidence: 81%

“…There are some algorithms that combine color and texture features together, such as the integrative co-occurrence matrix [12], texton co-occurrences matrix [13], multi-texton histogram (MTH) [4], color edge co-occurrence histogram (CECH) [14], color auto-correlograms [5], etc. Although computing Gabor features separately for each channel can be used as a colortexture descriptor, the computational burden of Gabor filtering is relatively big.…”

Section: Related Workmentioning

confidence: 99%

“…MTH [4] combines the first-and second-order statistics into an entity for the texton analysis, and thus the texture discrimination power is greatly increased. The MTH can represent the spatial correlation of edge orientation and color based on textons analysis [4].…”

Section: Color Quantization Levelsmentioning

confidence: 99%

“…The MTH can represent the spatial correlation of edge orientation and color based on textons analysis [4]. Its performance is better than that of Gabor filter.…”

Section: Color Quantization Levelsmentioning

confidence: 99%

See 4 more Smart Citations

Image retrieval based on micro-structure descriptor

Liu

Zhang

et al. 2011

Pattern Recognition

Self Cite

299

203

View full text Add to dashboard Cite

a b s t r a c tThis paper presents a simple yet efficient image retrieval approach by proposing a new image feature detector and descriptor, namely the micro-structure descriptor (MSD). The micro-structures are defined based on an edge orientation similarity, and the MSD is built based on the underlying colors in microstructures with similar edge orientation. With micro-structures serving as a bridge, the MSD extracts features by simulating human early visual processing and it effectively integrates color, texture, shape and color layout information as a whole for image retrieval. The proposed MSD algorithm has high indexing performance and low dimensionality. Specifically, it has only 72 dimensions for full color images, and hence it is very efficient for image retrieval. The proposed method is extensively tested on Corel datasets with 15,000 natural images. The results demonstrate that it is much more efficient and effective than representative feature descriptors, such as Gabor features and multi-textons histogram, for image retrieval.Crown

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 81%

Section: Related Workmentioning

confidence: 99%

Section: Color Quantization Levelsmentioning

confidence: 99%

“…The MTH can represent the spatial correlation of edge orientation and color based on textons analysis [4]. Its performance is better than that of Gabor filter.…”

Section: Color Quantization Levelsmentioning

confidence: 99%

See 3 more Smart Citations