Abstract
The segmentation of color images as a preprocessing to recognize objects is an important computer vision technique for robotic environment modeling. Linking image sequences to identify all the segments belonging to the same objects is a crucial and challenging problem, especially given the large volumes of image data. In this paper, we propose an aggregation-based fast K-medoids clustering algorithm as a solution for an efficient as well as reliable image segmentation and object recognition problem.
Highlights
To allow things to be recognized through the process of assembling sensory information into a useful and reliable representation of the world, there are many methods available for feature binding and sensory segmentation in practice
We have proposed a fast aggregation-based K-medoids clustering algorithm and applied it to image segmentation tasks
Our algorithm applies the proposed K-medoids algorithm to segment each image in an image sequence and uses CLARA algorithm for cluster merging
Summary
To allow things to be recognized through the process of assembling sensory information into a useful and reliable representation of the world, there are many methods available for feature binding and sensory segmentation in practice. We propose an aggregation based K-medoids clustering algorithm, which, when combined with CLARA algorithm, manifests its computational efficiency and competency with state-of-the-art partition-based clustering techniques It works by first dividing a dataset into several partitions to each of which K-medoids is applied to decrease computation time, and subsequently integrating the clustering results using CLARA algorithm. A third important contribution of the proposed method is its significant spatial efficiency in the sense that our methods only need to temporarily store a small number of data points during the clustering process, and can release them when not needed This third contribution is an advantage over standard K-medoids clustering methodologies, which requires to hold in main memory all the data points and their pairwise dissimilarity, which is otherwise seldom feasible when memory is limited.
Sign-in/Register to view highlights & summary 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.
