SEAM - an improved environmental adaptation method with real parameter coding for salient object detection

Abstract

Object detection is an important problem in computer vision attracting researchers from various domains. It deals with separating objects of interest from their background. Image-related low-level features are used to detect salient objects from images, however these features vary from image to image. There is no single feature that can clearly identify salient objects in all the images. Rather, a linear combination of these features may be useful for detection of salient objects. The computation of optimal weights of these features in a learning algorithm is an NP-Hard problem and requires an approximation of an optimization problem. So in the first phase, three low-level features capturing the edge and color information are extracted from the image. In the second phase, these features are integrated to form the saliency map. The linear weights are used for the combination which guides the dominance of feature(s). Thereafter, a threshold is applied over the saliency map to extract the salient objects. We use a linear weight vector and threshold that play a vital role in increasing the detection accuracy of the model. Identification of these parameters can be mapped to an optimization problem to identify the optimal linear weights best suited for the image. The problem is solved using an improved environmental adaptation method. There are three contributions of the paper. First being the identification of weight vector so that important feature(s) for an image are selected where the salient object is highlighted. The second is posing the salient object detection as an optimization problem with a relevant fitness function. Finally the third is utilization of Environment Adaptation Method (EAM) to solve the optimization problem pointed in the paper. The model is extensively validated over six complex datasets and achieves good results on standard performance measures used in comparison to twenty six related works. Four variants of the optimization algorithm are also presented. The EAM optimizer outperforms the GA, PSO and GWO optimizers.