Object- based Image Classification using Ant Colony Optimization and Fuzzy Logic

  • et al.
Citations of this article
Mendeley users who have this article in their library.
Get full text


Image analysis enables to get meaningful information from a digital image by applying the image processing techniques. During the process of extracting of this meaningful information number of challenges needs to be addressed for a high-resolution image. These high-resolution images which contain minimum of 300 pixels per inch are also known as ‘coarse’ images. One common problem of coarse image is it combines the spectral properties of intermixed pixels. This nature of coarse image will lead to ambiguity in grouping the pixels into clusters which are in turn constitute to different objects in the input image. To reduce this ambiguity in classifying or grouping the pixels, Ant Colony Optimization and Fuzzy Logic which is a Hybrid classification technique is proposed. The ACO has solved many classification problems. In this paper ACO and Fuzzy based to group the pixels into meaningful groups for coarse image and results are compared with various other unsupervised classification methods such as ISODATA and K-means




Rao K*, S., Rao N, S., & P, S. (2019). Object- based Image Classification using Ant Colony Optimization and Fuzzy Logic. International Journal of Innovative Technology and Exploring Engineering, 9(2), 315–319. https://doi.org/10.35940/ijitee.b6283.129219

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free