Abstract
Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the HSV color model in order to improve the accuracy of the results obtained from the color segmentation using the K-means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the H equation in order to avoid undefined conditions and increase robustness in the color model.
Highlights
This is the reason for the present proposal, which is developed in the spirit of improving a common process performed in image analysis using machine learning
A few images from the selected test dataset are exposed in order to demonstrate how every color performs in the segmentation done by K-means++
Some metrics measuring the performance are given: (i) Mean BAcc: the average balanced accuracy using all the data from all the clusters and all the iterations (ii) Best BAcc: the best individual balanced accuracy for one cluster occurring in the iterations (iii) Mean sen.: the average sensitivity (iv) Mean spe.: the average specificity (v) Avg. time: the running time for the algorithm given in seconds
Summary
Machine learning application area is growing every day, so it is possible to find applications using machine learning in health areas [1,2,3,4,5], system behavior predictions [6,7,8,9,10], image and video analysis [11,12,13,14,15], and speech and writing recognition [16,17,18,19,20], just to mention some of the most notable and recent applications. Despite the huge improvements done in machine learning in the recent years, it still requires more work and research This can be stated from the fact that a total accuracy is not yet achieved by machine learning, and sometimes the results are still not usable. This is the reason for the present proposal, which is developed in the spirit of improving a common process performed in image analysis using machine learning. Using the BSDS500 dataset gives a more reliable ground in the testing cases
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
