Research on Salient Object Detection Using Deep Learning and Segmentation Methods
M. Indirani1, S. Shankar2
1M. Indirani, Assistant Professor, Department of IT, Hindusthan College of Engineering and Technology, Coimbatore (Tamil Nadu), India.
2Dr. S. Shankar, Professor & Head, Department of CSE, Hindusthan College of Engineering and Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 280-287 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10460982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1046.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection has attracted a lot of interest in computer vision and recently various heuristic computational models have been designed. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. The aim of this review work is to study about the details of methods in salient object detection. It not only focuses on the methods to detect saliency objects, but also reviews the works related to spatio temporal video attention detection technique in video sequences. It also discusses the open issues in terms of evaluation metrics and dataset bias in model performance and suggests future research directions. The evaluation metrics are classified into mean absolute error (MAE), Accuracy and Run-Time complexity.
Keywords: Spatiotemporal Constrained Optimization Model (SCOM), Context-Aware (CA), Graph-Based Manifold Ranking (GMR), Bootstrap Learning (BL), Deep Learning.
Scope of the Article: Deep Learning