Title :
An improved FCM algorithm for ripe fruit image segmentation
Author :
Anmin Zhu ; Liu Yang
Author_Institution :
Sch. of Comput. Sci. & Software Eng., Shenzhen Univ., Shenzhen, China
Abstract :
Fuzzy C-Means (FCM) clustering algorithm has been widely used in the field of image segmentation with its good clustering efficiency. However, it may cause a time-consuming result and even convergence to local minima because of its local search character while with an improper initial value. Therefore, an improved FCM algorithm is proposed in this paper to solve the ripe fruit image segmentation problem. In the proposed approach, the concept of the neighborhood density is introduced to initialize the cluster center to avoid the improper initial value, which is based on the neighborhood correlation of the data space. Then a resample image method based on entropy constraint is used to reduce the data set, so that the clustering time will be reduced. The effectiveness and efficiency of the proposed approach are demonstrated by experimental studies with some standard data sets and real tomato images.
Keywords :
agricultural products; entropy; fuzzy set theory; image sampling; image segmentation; pattern clustering; production engineering computing; FCM algorithm; cluster center; data space neighborhood correlation; entropy constraint; fuzzy c-means clustering algorithm; local minima; local search character; neighborhood density; resample image method; ripe fruit image segmentation; tomato image; Accuracy; Clustering algorithms; Color; Entropy; Histograms; Image color analysis; Image segmentation; color image segmentation; entropy constraint; fuzzy c-means clustering; neighborhood density;
Conference_Titel :
Information and Automation (ICIA), 2013 IEEE International Conference on
Conference_Location :
Yinchuan
DOI :
10.1109/ICInfA.2013.6720338