Performance Analysis of Structured Matrix Decomposition with Contour Based Spatial Prior for Extracting Salient Object from Complex Scene

Abstract

 Automatically extracting most conspicuous object from an image is useful and important for many computer vision related tasks. Performance of several applications such as object segmentation, image classification based on salient object and content based image editing in computer vision can be improved using this technique. In this research work, performance of structured matrix decomposition with contour based spatial prior is analyzed for extracting salient object from the complex scene. To separate background and salient object, structured matrix decomposition model based on low rank matrix recovery theory is used along with two structural regularizations. Tree structured sparsity inducing regularization is used to capture image structure and to enforce the same object to assign similar saliency values. And, Laplacian regularization is used to enlarge the gap between background part and salient object part. In addition to structured matrix decomposition model, general high level priors along with biologically inspired contour based spatial prior is integrated to improve the performance of saliency related tasks. The performance of the proposed method is evaluated on two demanding datasets, namely, ICOSEG and PASCAL-S for complex scene images. For PASCAL-S dataset precision recall curve of proposed method starts from 0.81 and follows top and right-hand border more than structured matrix decomposition which starts from 0.79. Similarly, structural similarity index score, which is 0.596654 and 0.394864 without using contour based spatial prior and 0.720875 and 0.568001 using contour based spatial prior for ICOSEG and PASCAL-S datasets shows improved result.