Abstract
Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.
Highlights
Image segmentation, which partitions images into multiple regions with similar characteristics, is commonly used for image analysis and understanding
We can observe that our method provided much more satisfactory results than K-means and Fuzzy C-Means (FCM) clustering, for the rhinoceros and horses images where the illumination of the foreground was homogeneous with that of background
Visual inspection demonstrates that our method out-performed both conventional and state-of-the-art clustering methods
Summary
Image segmentation, which partitions images into multiple regions with similar characteristics, is commonly used for image analysis and understanding. Lei et al [15] introduced a Fast and Robust Fuzzy C-Means (FRFCM), which uses morphological reconstruction to smooth the image and employs a faster membership to calculate the distance between the pixels and cluster centers These local information-based clustering algorithms such as FLICM and FRFCM clustering are not robust to illumination changes, i.e., pixels with identical colors, but different illuminations may be segmented into different segments (sets of pixels), which limits their real-world applications. Delibasis et al [17] proposed a video segmentation robust to change in illumination based on different background models These approaches achieve favorable performance on many video sequences, the supervised pre-learned background model limits their application in single image segmentation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
