Image Retrieval Using Maximum Frequency of Local Histogram Based Color Correlogram

Rasheed, Waqas; An, Yi; Pan, Sung Bum; Jeong, Ilhoe; Park, Jong-An; Jinsuk, Kang

doi:10.1109/mue.2008.27

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…Color correlogram is a spatial extension of the histogram. It is usually used for image retrieval [18][19]. On the other hand, color moment is simple and robust since it is insensitive to color variations [17][18].…”

Section: Introductionmentioning

confidence: 99%

Palm oil fresh fruit bunch ripeness grading identification using color features

Sabri¹,

Ibrahim²,

Syahlan³

et al. 2018

J. Fundam and Appl Sci.

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Section: Introductionmentioning

confidence: 99%

Palm oil fresh fruit bunch ripeness grading identification using color features

Sabri¹,

Ibrahim²,

Syahlan³

et al. 2018

J. Fundam and Appl Sci.

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“…To verify the superiority of the proposed method, different color feature extraction methods in previous 8 and CC. 9 In Yang et al, 24 DCR was extracted in RGB color space. In Xiang et al, 15 DCH was extracted without merging the non-DCs into the nearest DC sets.…”

Section: Comparison Resultsmentioning

confidence: 99%

“…The global color feature focuses on color indexing techniques based on global color distributions, such as the color histogram (CH), 4 dominant color (DC), 5 color moment (CM), 6 color distribution entropy (CDE) 7 and so on, but has limited discriminating ability to capture local color information. The spatial color feature combines the spatial correlation of color regions and the global distribution, including the color coherence vector (CCV), 8 color correlogram (CC) 9 and annular, angular and hybrid histograms, 10 but requires more computation time than the global color feature. The local color feature is another method describing the spatial structure relations of colors.…”

mentioning

confidence: 99%

Image retrieval of wool fabric. Part II: based on low-level color features

Zhang

Xiang

Wang

et al. 2019

Textile Research Journal

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Color is difficult to distinguish by human vision and is described by keywords, resulting in low efficiency of wool fabric retrieval in factories at present. To obtain the process sheets of existing products and reduce the work of color measurement in sample analysis, this paper proposes an effective method based on dominant colors (DCs) and color moments (CMs) for wool fabric image retrieval. Firstly, the image was scaled to reduce computational time. Then, the hue, saturation, value color space was divided into 128 parts by the fast color quantization algorithm to extract the DCs of the image. Meanwhile, the CMs based on image partition were calculated in CIE L* a* b* color space to describe the spatial color information. Subsequently, different similarity measure methods were carried out based on the DC feature and CM feature. Finally, experiments were conducted on a wool fabric image database with 20,000 images for parameter optimization and verification. The average precision and recall were up to 87% and 44%, respectively. Experimental results show that the proposed scheme can retrieve images with the same or similar colors quickly and effectively and it outperformed other methods, providing referential assistance for the factory worker when retrieving wool fabrics.

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“…There are different methods for color feature based image retrieval. Color Correlogram [1,2,3,4,5] outperform all the previous approaches. Correlogram can be stored as a 3-dimensional table where first two dimensions are number of possible color pairs of an image, whereas in third dimension is the distance between color pairs.…”

Section: Related Workmentioning

confidence: 85%

Heuristic and intermediate features based image retrieval

Hazra¹,

Bhattacharyya²,

Shin³

2014

ces

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In this paper, a new method for heuristics and intermediate features based image retrieval is proposed. Heuristic features are identified and directly stored into the database and easily retrieved also. An algorithm is used to convert low level features hue, saturation and intensity in HSI space to semantic based color names. Images can be retrieved by these semantic color names. For semantics based texture queries, statistical values of textures calculated from the image are used as the low level features in image retrieval. But these low level features are mostly not understandable by the general human. There is always a semantic gap between human understanding and low level features. Image database is constructed with low level texture features obtained from Gray Level Co-Occurrence 1732 Dipankar Hazra et al. Matrix (GLCM). Semantic level queries from the user mapped to the low level features at retrieval time to retrieve the required images.

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Image Retrieval Using Maximum Frequency of Local Histogram Based Color Correlogram

Cited by 12 publications

References 14 publications

Palm oil fresh fruit bunch ripeness grading identification using color features

Palm oil fresh fruit bunch ripeness grading identification using color features

Image retrieval of wool fabric. Part II: based on low-level color features

Heuristic and intermediate features based image retrieval

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