CHELM: Convex Hull based Extreme Learning Machine for salient object detection

Abstract

Machine learning based saliency detection methods have achieved better performance than traditional methods. Here, we propose a machine learning based method that utilizes Convex Hull and Extreme Learning Machine (ELM) for detecting salient object(s) in an image. The novelty of the proposed method lies in the generation of training set without using human annotations. Initially, an input image is segmented using SLIC algorithm at different scales to produce multiscale segmented images. This is followed by estimating two different saliency priors viz. (a) Convex Hull center prior and (b) contrast prior for each segmented image. These priors exploit foreground center and spatially weighted contrast respectively. Both of these estimated priors help in computing initial saliency of each segment across all scales. For each scale, the initial saliency map along with the Convex Hull based label map is employed on a segmented image to determine the positive (salient) and negative (background) training set. Distinctive features for each segment belonging to the training set are extracted and then passes to the Extreme Learning Machine for learning the ELM model. Afterwards, multiscale saliency maps of an image are found by applying the learned ELM model on distinctive features extracted from each segment across multiple scales. These multiscale saliency maps are linearly combined to obtain the final saliency map. The effectiveness of the proposed method is supported through extensive experimental results performed on six publicly available datasets viz. MSRA10K, DUT-OMRON, ECSSD, PASCAL-S, SED2, and THUR15K. The performance of the proposed method was compared with 11 state-of-the-art methods in terms of Precision, Recall, F-Measure, Receiver Operating Characteristics (ROC), and Area under the curve (AUC). The proposed method outperforms or comparable with compared methods in terms of all the performance measures.