Object- based Image Classification using Ant Colony Optimization and Fuzzy Logic
Subba Rao K1, Sambasiva Rao N2, Sammulal P3

1Subba Rao K, PhD Scholar, JNTU Hyderabad, Telangana, India.
2Sambasiva Rao N, Professor & Principal, SBIT, Khammam, Telangana, India.
3Sammulal P, Professor, Department of CSE, JNTUCEJ, JNTU Hyderabad, Telangana, India. 

Manuscript received on November 14, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 315-319 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6283129219/2019©BEIESP | DOI: 10.35940/ijitee.B6283.129219
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Abstract: 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 
Keywords:  Classification, ACO, Fuzzy Logic, Object-based Image Analysis, Coarse image
Scope of the Article: Fuzzy Logic