A Color Image Segmentation Method Based on Improved K-Means Clustering Algorithm

Jin, Ran; Kou, Chunhai; Liu, Ruijuan; Li, Yefeng

doi:10.1007/978-1-4471-4850-0_63

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…K‐means clustering algorithm takes distance as the similarity evaluation index and divides the data into different clusters according to distance, which is one of the simplest clustering algorithms at present and can be used to segment images [33]. In this paper, the K‐means algorithm was used to cluster the feature vector matrix F of the image I , and the segmentation of the jacquard pattern was realised.…”

Section: Methodsmentioning

confidence: 99%

Novel segmentation algorithm for jacquard patterns based on multi‐view image fusion

et al. 2020

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Pattern regeneration is one of the applications of reverse engineering technology in the textile field, which realises the process of textile‐pattern regeneration‐textile, and fundamentally provides an intelligent design means of textile. At present, the method of pattern regeneration in the jacquard fabric is to use the image segmentation algorithm to segment the image digitalised by unidirectional imaging, and then the segmented pattern could be identified to regeneration for the design of new fabrics. However, due to the concave and convex pattern textures on the surface of jacquard fabric, the traditional unidirectional imaging method cannot be used for the full characterisation of its structural information, resulting in unsatisfactory pattern segmentation effect. To solve this problem, a novel segmentation algorithm for jacquard patterns based on multi‐view image fusion was proposed in this study. Based on multi‐view image acquisition and fusion, the pattern image of jacquard fabric could be cluster‐segmented by extracting the complete texture information of the fused image and the actual colour information of the calibrated image. Compared with the traditional unidirectional imaging method, the experimental results show that the enhanced texture information of the fused image is more workable for the pattern segmentation, it validates the effectiveness of the proposed method.

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Section: Methodsmentioning

confidence: 99%

Novel segmentation algorithm for jacquard patterns based on multi‐view image fusion

et al. 2020

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“…In this paper, the K-means clustering segmentation based on Lab color space is used to extract fruit region from fruit images. Compared to RGB color space, Lab color space can more accurately convey the human eye's perception of color [9]. It separates brightness (L channel) and color (a and b channels), making color information more clear and accurate.…”

Section: The K-means Clustering Segmentation Algorithm Based On Lab C...mentioning

confidence: 99%

Super-resolution for the extracted region of fruit image

Chen,

Huang,

Zhang

2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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This paper proposes a method for super-resolution reconstruction of fruit images. Firstly, in order to eliminate background interference and reconstruct fruit regions more targeted, the K-means clustering algorithm based on Lab color space is used to automatically extract the fruit regions from whole fruit images. This method can be applied to fruit images with different backgrounds. Then, the ANR model is used to achieve fast reconstruction for the fruit region image. Finally, a global error compensation optimization algorithm is used to further correct global brightness or color errors in the fruit region image and improve its overall quality. The experimental results show that the algorithm proposed can accurately extract regions of interest for fruits in images with different complex backgrounds. Meanwhile, PSNR data indicates that the image reconstruction and optimization algorithm proposed has certain advantages and practicality.

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“…Through literature exists two main methods to extract dominant colors; clustering [29,37] and quantization [38,39]. Regards clustering the general idea is to group similar pixel colors into set of clusters, where each cluster is represented by its centroid [40], which acts as the dominant color.…”

Section: A Extraction Of Dominant Colorsmentioning

confidence: 99%

Compact Signature-Based Compressed Video Matching Using Dominant Color Profiles (DCP)

Bekhet

Ahmed

2014

2014 22nd International Conference on Pattern Recognition

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A Color Image Segmentation Method Based on Improved K-Means Clustering Algorithm

Cited by 10 publications

References 9 publications

Novel segmentation algorithm for jacquard patterns based on multi‐view image fusion

Novel segmentation algorithm for jacquard patterns based on multi‐view image fusion

Super-resolution for the extracted region of fruit image

Compact Signature-Based Compressed Video Matching Using Dominant Color Profiles (DCP)

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