Object Segmentation using Local Histograms, Invasive Weed Optimization Algorithm and Texture Analysis

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.

Keywords:

Object segmentation , Local threshold , Histogram , invasive weed optimization , Texture analysis