NDLI: A novel combination of second-order statistical features and segmentation using multi-layer superpixels for salient object detection

Content Provider	Springer Nature Link
Author	Arya, Rinki Singh, Navjot Agrawal, R. K.
Copyright Year	2016
Abstract	Salient object detection is one of the outstanding capabilities of the human visual system (HVS). The researcher community aims at developing a salient object detection model that matches the detection accuracy as well as computation time taken by the humans. These models can be developed in either spatial domain or frequency domain. Spatial domain models provide good detection accuracy at the cost of high computational time while frequency domain models offer fast computational speed to meet real-time requirements at the cost of poor detection accuracy. In order to induce a trade-off between computational time and accuracy, we propose a model which provides high detection accuracy without taking much of computation time. To detect the salient object with an accurate shape, we first segment the given image by utilizing a bipartite graph partitioning approach which aggregates multi-layer superpixels in a principled and effective manner. Second, the saliency of each segmented region is computed based on a hypercomplex Fourier transform (HFT) saliency map reconstructed using amplitude spectrum, filtered at an appropriate scale chosen using statistical features extracted from grey- level co-occurrence matrix and original phase spectrum. Finally, a saliency map is generated by taking average of the HFT coefficients of each region in the segmented image and then using the average HFT intensity value of the entire image as a threshold to clearly separate salient object from the background. The performance of the proposed model is evaluated in terms of F–measure, area under curve (AUC), and computation time using six publicly available image datasets. Both qualitative and quantitative evaluations on six publicly available datasets demonstrate the robustness and efficiency of the proposed model against twenty popular state-of-the-art methods.
Starting Page	254
Ending Page	271
Page Count	18
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	46
Issue Number	2
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2016-08-20
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Salient object detection Visual saliency Gray-level Co-occurrence Matrix (GLCM) Hypercomplex fourier transform (HFT) Superpixel Bipartite graph Segmentation Saliency map Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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