Publication Type : Journal Article
Publisher : Journal of Computational Science-Elsevier
Source : Journal of Computational Science-Elsevier, Vol 25, pp351-366, 2017
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032367327&doi=10.1016%2fj.jocs.2017.07.007&partnerID=40&md5=25186033c4335a5e6f56bde2c2ad3aa8
Keywords : Area under roc curve (AUC), Behavioral research, Benchmarking, Color, Color image processing, Correlation methods, Dynamic mode decompositions, Human Visual System, Image processing, Image representations, Image segmentation, Object Detection, Pearson's correlation coefficients, Precision and recall, Salient region detections, Standard performance
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication
Year : 2017
Abstract : Estimation of visual saliency in images has become an important tool since it allows the processing of images without knowing the actual contents. In this paper we introduce a novel method to detect salient regions of an image using dynamic mode decomposition (DMD). The key idea is to utilize the analytical power of DMD, which is a powerful tool evolving in data science. The applicability of DMD in static image processing applications is made possible by developing a new way of image representation. The proposed algorithm utilizes color and luminance information to generate a full resolution saliency map. In order to model the non-linear behavior of human visual system we exploited the power of different color spaces including CIELab, YCbCr, YUV and RGB. The proposed method is computationally less expensive, simple and generates full resolution saliency maps.The effectiveness of the generated saliency map is evaluated and confirmed on three benchmark data sets across fourteen existing algorithms based on the standard performance measures such as F-measure, precision and recall curve, mean absolute error (MAE), area under ROC curve (AUC-Borji), normalized scanpath saliency (NSS) and Pearson's correlation coefficient (CC). We also propose a saliency driven transition region [SDTR] based segmentation to segment the salient object from images.
Cite this Research Publication : O K Sikha, Sachin Kumar S, K P Soman, Salient Region Detection and Object Segmentation in Color Images using Dynamic Mode Decomposition, Journal of Computational Science-Elsevier, Vol 25, pp351-366, 2017