Abstract
Salient object detection essentially deals with various image processing and video saliency methodologies such as object recognition, object tracking, and saliency refinement. When image contains diverse object parts with cluttered background then using background prior we perform salient object detection through which we get more accurate and robust saliency maps. This paper introduces the analysis of salient object detection using synthetic dataset which also deals with negative interference of image that contains diverse object parts with cluttered background. Earlier study uses contrast prior but nowadays researchers use mainly boundary connectivity for improving the results. So, for detecting salient object we used four stages: first, we use SLIC superpixel method for image segmentation. Second, we use boundary connectivity which distinguishes the spatial layout of image region by considering image boundaries. Third, we use background measure and for reducing the noise in both foreground and background regions. Lastly, we use optimization framework through which we acquire a clean saliency map.
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Aswar, A., Manjaramkar, A. (2019). Salient Object Detection for Synthetic Dataset. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_131
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DOI: https://doi.org/10.1007/978-3-030-00665-5_131
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