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

Most of the methods proposed for segmenting image objects are the supervised methods which are costly due to their requirement for large amounts of labeled data. However, in this article, we present a method for segmenting objects based on a meta-heuristic optimization that does not require any training data. This procedure consists of the two main stages of edge detection and texture analysis. In the edge detection stage, we utilize invasive weed optimization and local thresholding. The edge detection methods that are based on the 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 the automatic determination of these parameters using an evolutionary algorithm. The evaluation of this method demonstrates its high performance on natural images.