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• W4285137649 abstract "Techniques for detecting salient objects mimic human behavior by recognizing the most noticeable parts of images as objects. Salient object detection has attracted many researchers&#x2019; attention nowadays for various computer vision and pattern recognition applications. In this paper, a unique approach is proposed based on the global and local saliency detection using wavelet transform and hybridizing it with learning-based saliency detection using a guided filter. First, the input image is subjected to superpixel segmentation to achieve visually uniform regions and to reduce the computational cost. The global and local saliency maps are then generated using global and local features extracted by the wavelet transform of the segmented image, as the wavelet transform gives a multiscale analysis of images in frequency as well as in spatial domain. The learning-based saliency maps are generated using random forest regression which considers the location, color, and textural features of the segmented image. The global and local saliency maps are fused to generate the wavelet-based saliency map which is further hybridized with the saliency map generated using random forest regression. The paper discusses the novel technique for hybridizing wavelet-based and learning-based saliency maps using a guided filter-based attention map generation. Several experiments are conducted on five different saliency datasets containing images with complex backgrounds, multiple objects, and low contrast. To evaluate the efficacy of the proposed method, extensive qualitative and quantitative performance analysis is carried out. Experimental results validate the significant improvement in the detection of salient regions as compared to the state-of-the-art methods." @default.
• W4285137649 created "2022-07-14" @default.
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• W4285137649 date "2022-01-01" @default.
• W4285137649 modified "2023-10-14" @default.
• W4285137649 title "Boundary Preserved Salient Object Detection Using Guided Filter Based Hybridization Approach of Transformation and Spatial Domain Analysis" @default.
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• W4285137649 doi "https://doi.org/10.1109/access.2022.3185409" @default.
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