摘要
为了区分印花织物上彩色图案的色彩区,满足行业对织物色彩的需求,提出一种SOM神经网络与改进型K-均值算法相结合的印花织物自动分色系统.在三维RGB颜色空间内,利用遗传算法搜寻出代表原织物颜色分布的子图像,并采用两层聚类方法完成分色:第一层使用SOM神经网络对大于期望聚类数的数据样本进行初始聚类;第二层对SOM神经网络初始聚类的神经元利用改进型K-均值算法进一步聚类,形成最终的聚类结果,从而完成分色.实验结果显示,此系统能够准确地区分出印花织物图案的色彩,并完成自动分色.
A printed fabric automatic separation system based on SOM neural networks and im- proved K-means algorithm is proposed, which can distinguish the color of color patterns on printed fabric to meet industry demand for fabric colors. In three dimensional RGB color space, genetic algorithm is used for sub-images which represent the original fabric color distribution, thereby reducing the computation of color separation. Then two layers of clustering method is used to complete color separation, the first layer uses SOM neural network for a large number of data samples to complete the initial cluster, which was much larger than expected number of clusters. The second layer uses the improved K-means algorithm to further clustering neurons, which are through the initial cluster of SOM neural network. Thus final clustering results are obtained and the separation is completed. Experimental results show that this system can accu- rately distinguish colors of pattern on printed fabric and complete automatic color separation.
出处
《西安工程大学学报》
CAS
2016年第1期52-57,共6页
Journal of Xi’an Polytechnic University
基金
国家自然科学基金资助项目(61301276)
西安工程大学博士科研启动基金资助项目(BS1416)
西安工程大学学科建设资助项目(107090811)
西安工程大学青年学术骨干支持计划资助项目(CX12573)
