Content-based image retrieval using color moment and Gabor texture feature

Huang, Zhi-Chun; Chan, Patrick P. K.; Ng, Wing W. Y.; Yeung, Daniel

doi:10.1109/icmlc.2010.5580566

Cited by 134 publications

(57 citation statements)

References 14 publications

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“…A method based on color and texture features is proposed in [22]. As color feature, they use color moments of the Hue, Saturation and Value (HSV) of the image and Gabor descriptors are adopted as texture features.…”

Section: Fig 1: Block Diagram Of Cbirmentioning

confidence: 99%

Image Retrieval based on the combination of Color Histogram and Color Moment

Singh¹,

Hemachandran²

2012

IJCA

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A novel technique for Content based image retrieval (CBIR) that employs color histogram and color moment of images is proposed. The color histogram has the advantages of rotation and translation invariance and it has the disadvantages of lack of spatial information. In this paper, to improve the retrieval accuracy, a content-based image retrieval method is proposed in which color histogram and color moment feature vectors are combined. For color moment, to improve the discriminating power of color indexing techniques, a minimal amount of spatial information is encoded in the color index by dividing the image horizontally into three equal nonoverlapping regions. The three moments (mean, variance and skewness) are extracted from each region (in this case three regions), for all the color channels. Thus, for a HSV color space, 27 floating point numbers are used for indexing. The HSV (16, 4, 4) quantization scheme has been adopted for color histogram and an image is represented by a vector of 256-dimension. Weights are assigned to each feature respectively and calculate the similarity with combined features of color histogram and color moment using Histogram intersection distance and Euclidean distance as similarity measures. Experimental results show that the proposed method has higher retrieval accuracy in terms of precision than other conventional methods combining color histogram and color moments based on global features approach

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Section: Fig 1: Block Diagram Of Cbirmentioning

confidence: 99%

Image Retrieval based on the combination of Color Histogram and Color Moment

Singh¹,

Hemachandran²

2012

IJCA

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“…In this paper, we apply Gabor filters to extract robust features from lawn scenes, as Gabor filters are successfully practiced in the fields of texture classification [5][6] [7] and object recognition [8] [9], which is confirmed to have a better performance than other descriptors like LBP. Consider that Gabor features are of so high dimensionality that it is impractical to use Gabor features directly, we proposed PCA methods [10] to compress the feature and extract components that have better classification performance.…”

Section: Introductionmentioning

confidence: 99%

Vision-based Lawn Boundary Recognition for Mowing Robot

Guo¹,

Sun²

2016

Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

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Abstract-We present a novel method for mowing robot's lawn boundary recognition based on Gabor filters and support vector machine (SVM). Robust texture features of images are extracted and concatenated using Gabor filters. The principle components analysis (PCA) approach is then used to reduce the dimensionality of Gabor features. Based on the compressed features, SVM model is trained and used to perform the grass texture classification task. The boundary of lawn is then recognized according to the ratio of grass area of the image. To demonstrate the effectiveness and robustness of our proposed method, a dataset is created with about 1500 images of different lawn scenes. Result shows that a classification accuracy of 96.7% can be reached when SVM is used. Experiments of the lawn boundary recognition have also been conducted on the mowing robot under different lighting conditions. The recognition rate tested is 98.3%, which proves the efficiency and superiority of our proposed method.

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“…The disease appears as small yellow leaf spots that later turn brown with bluish-black lace-like markings. Under wet conditions, a white downy mold develops in the underside of the leaf [3]. The leaf spots render the radishes unmarketable.…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Vision System for Radish Crop Quality Monitoring based on Leaf Inspection

Khatra¹,

Kaur²

2012

IOSRJEN

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The crop of radish is very often infected by a disease that leaves spots of brown, gray or off-white colors on the radish leaves in winter. Scientifically, this disease is known as cercospora leaf spot or cercospora cruciferarum. It's a kind of fungus that often kills young seedlings. The fungus spreads by air and can also infect radish plants when splashed onto leaves during a rainfall as the radish grows up down the soil as stem and leaves come out of the soil. Therefore, it is important to monitor the leaf at regular intervals so as to keep track on quality of growing radish crop. In the presented paper, a novel machine vision system has been proposed that visually inspects the leaves coming out of the soil and based on spots on leaves, it determines the nature of fungus and its depth into the radish stem. The size of the fungus, color depth and location and locus of the fungus on leaves gives an accurate determination of crop quality under the soil. In the presented thesis work, the image of the crop leaves are taken by a good quality color camera and processed for getting a gray colored and segmented image depending upon the nature and size of the fungus. A criterion is set for acceptable and rejects crop quality based on the fungus level.

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Content-based image retrieval using color moment and Gabor texture feature

Cited by 134 publications

References 14 publications

Image Retrieval based on the combination of Color Histogram and Color Moment

Image Retrieval based on the combination of Color Histogram and Color Moment

Vision-based Lawn Boundary Recognition for Mowing Robot

A Novel Machine Vision System for Radish Crop Quality Monitoring based on Leaf Inspection

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