TY - JOUR ID - 2135 TI - Object Segmentation using Local Histograms, Invasive Weed Optimization Algorithm and Texture Analysis JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Bayatpour, S. AU - Hasheminejad, Seyed M. H. AD - Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran. Y1 - 2021 PY - 2021 VL - 9 IS - 4 SP - 439 EP - 449 KW - Object segmentation KW - Local threshold KW - Histogram KW - invasive weed optimization KW - Texture analysis DO - 10.22044/jadm.2021.10200.2158 N2 - Most of the methods proposed for segmenting image objects are supervised methods which are costly due to their need for large amounts of labeled data. However, in this article, we have presented a method for segmenting objects based on a meta-heuristic optimization which does not need any training data. This procedure consists of two main stages of edge detection and texture analysis. In the edge detection stage, we have utilized invasive weed optimization (IWO) and local thresholding. Edge detection methods that are based on local histograms are efficient methods, but it is very difficult to determine the desired parameters manually. In addition, these parameters must be selected specifically for each image. In this paper, a method is presented for automatic determination of these parameters using an evolutionary algorithm. Evaluation of this method demonstrates its high performance on natural images. UR - https://jad.shahroodut.ac.ir/article_2135.html L1 - https://jad.shahroodut.ac.ir/article_2135_6b81d4b9dec6d2a381de3e331d667ba5.pdf ER -