Salient object carving

In this paper, we propose an unsupervised two-stage algorithm to extract salient objects from images. In the first stage, the image is segmented into superpixels that are grouped together through k-means clustering, based on histogram features of superpixels. The saliency of each cluster is calculated using inter-cluster and intra-cluster feature dissimilarities. In the second stage, we use seam carving to obtain an object level segmentation of the image in the form of a bounding box around the salient object. We propose an automated approach for seam carving based on a novel energy function obtained by combining the saliency output with a texture removed input image. We compute the optimal number of seams to be removed to extract the salient object instead of manually providing it. The performance of the proposed method is demonstrated by processing different types of images.