{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T09:12:52Z","timestamp":1651828372089},"reference-count":17,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>Image segmentation is a method of segregating the image into required segments\/regions. Image thresholding being a simple and effective technique, mostly used for image segmentation, these thresholds are optimized by optimization techniques by maximizing the Tsallis entropy. However, as the two level thresholding extends to multi-level thresholding, the computational complexity of the algorithm is further increased. So there is need of evolutionary and swarm optimization techniques. In this article, first time optimal thresholds are obtained by maximizing the Tsallis entropy by using novel hybrid bacteria foraging optimization technique and particle swam optimization (hBFOA-PSO). The proposed hBFOA-PSO algorithm performance in segmenting the image is tested using natural and standard images. Experiments show that the proposed hBFOA-PSO is better than particle swarm optimization (PSO), the cuckoo search (CS) and the adaptive Cuckoo Search (ACS).<\/jats:p>","DOI":"10.4018\/ijcvip.2019010102","type":"journal-article","created":{"date-parts":[[2019,3,5]],"date-time":"2019-03-05T17:44:27Z","timestamp":1551807867000},"page":"17-34","source":"Crossref","is-referenced-by-count":0,"title":["Evolutionary Image Thresholding for Image Segmentation"],"prefix":"10.4018","volume":"9","author":[{"family":"Phanindra Kumar N.S.R.","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, AITAM, Tekkali, Srikakulam,, India"}]},{"family":"Prasad Reddy P.V.G.D.","sequence":"additional","affiliation":[{"name":"Department of CS&SE, Andhra University, Visakhapatnam, India"}]}],"member":"2432","reference":[{"key":"IJCVIP.2019010102-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2013.02.001"},{"key":"IJCVIP.2019010102-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.07.025"},{"issue":"3","key":"IJCVIP.2019010102-2","first-page":"236","article-title":"Hybrid Gravitational Search and Pattern Search based Image Thresholding by Optimizing Shannon and Fuzzy Entropy for Image Compression","volume":"3","author":"K.Chiranjeevi","year":"2017","journal-title":"International Journal of Image and Data Fusion"},{"key":"IJCVIP.2019010102-3","doi-asserted-by":"publisher","DOI":"10.5566\/ias.1611"},{"key":"IJCVIP.2019010102-4","doi-asserted-by":"crossref","unstructured":"Horng, M. H., & Jiang, T. W. (2010, October). Multilevel image thresholding selection based on the firefly algorithm. In 2010 7th international conference on Ubiquitous intelligence & computing and 7th international conference on autonomic & trusted computing (UIC\/ATC) (pp. 58-63). 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