Background dominant colors extraction method based on color image quick fuzzy c-means clustering algorithm

Abstract A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design. This paper proposes a Color Image Quick Fuzzy C-Means (CIQFCM) clustering algorithm based on clustering spatial mapping. First, the clustering sample space was mapped from the image pixels to the quantized color space, and several methods were adopted to compress the amount of clustering samples. Then, an improved pedigree clustering algorithm was applied to obtain the initial class centers. Finally, CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image. After theoretical analysis of the effect and efficiency of the CIQFCM algorithm, several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm. The results indicated that the value of quantization intervals should be set to 4, and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect. In addition, as the image size increased from 128 × 128 to 1024 × 1024, the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times, which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.

[1]  Lu-ping Xu,et al.  Improved Genetic FCM Algorithm for Color Image Segmentation , 2006, 2006 8th international Conference on Signal Processing.

[2]  Du Hua-yue.  Improvement and Application of Color Histogram , 2011 .

[3]  K. Song,et al.  Optical classification of inland waters based on an improved Fuzzy C-Means method. , 2019, Optics express.

[4]  Ramin Ranjbarzadeh,et al.  Automated liver and tumor segmentation based on concave and convex points using fuzzy c-means and mean shift clustering , 2020 .

[5]  Eli Saber,et al.  Survey of contemporary trends in color image segmentation , 2012, J. Electronic Imaging.

[6]  Y. Prasetyo,et al.  EVALUATING SOUTHEAST ASIAN MILITARY CAMOUFLAGE DESIGNS USING CAMOUFLAGE SIMILARITY INDEX (CSI) , 2020 .

[7]  Jong-Wha Chong,et al.  Improved nonlocal fuzzy color segmentation-based color reconstruction hybrid approach for white-RGB imaging systems , 2014, J. Electronic Imaging.

[8]  Arpita Das,et al.  A fast and automated segmentation method for detection of masses using folded kernel based fuzzy c-means clustering algorithm , 2019, Appl. Soft Comput..

[9]  Chiuhsiang Joe Lin,et al.  A metaheuristic‐based approach to optimizing color design for military camouflage using particle swarm optimization , 2019 .

[10]  Yijun Yan,et al.  Fusion of Dominant Colour and Spatial Layout Features for Effective Image Retrieval of Coloured Logos and Trademarks , 2015, 2015 IEEE International Conference on Multimedia Big Data.

[11]  Alexander Toet,et al.  Urban camouflage assessment through visual search and computational saliency , 2012 .

[12]  Bernard C. Jiang,et al.  Optimization of color design for military camouflage in CIELAB color space , 2019, Color Research & Application.

[13]  Wei-Han Chang,et al.  A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval , 2008, J. Vis. Commun. Image Represent..

[14]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[15]  Tad T. Brunyé,et al.  Masked priming for the comparative evaluation of camouflage conspicuity. , 2017, Applied ergonomics.

[16]  Ling Li,et al.  Research on extraction and reproduction of deformation camouflage spot based on generative adversarial network model , 2020 .

[17]  Doulaye Dembélé,et al.  Fuzzy C-means Method for Clustering Microarray Data , 2003, Bioinform..

[18]  Xiaoqi Liu,et al.  Detecting PHG frames in wireless capsule endoscopy video by integrating rough global dominate-color with fine local texture features , 2018, Medical Imaging.

[19]  Hong Yan,et al.  Saliency detection by using blended membership maps of fast fuzzy-C-mean clustering , 2019, International Conference on Machine Vision.

[20]  Sun Xiao-quan,et al.  Extracting dominant colors of imitative pattern painting with CIEDE2000 and pyramid FCM , 2010 .

[21]  Jasjit S. Suri,et al.  Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind , 2016, Comput. Methods Programs Biomed..

[22]  James C. Bezdek,et al.  Efficient Implementation of the Fuzzy c-Means Clustering Algorithms , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.