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Parameter adaptive pulse coupled neural network-based saliency map fusion strategy for salient object detection

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Abstract

Today, salient object detection has caught the interest of numerous researchers for a variety of applications in computer vision. Most deep learning-based algorithms for SOD tasks produce excellent results but require a lot of data availability and large computational and structural complexities. Also, many methods provide excellent outcomes but are unable to preserve the complete boundaries of the objects in images. The paper discusses an innovative integration method for salient object detection of superpixel segmented images to address these issues. It deals with the integration of saliency maps generated by image decomposition based on non-sub-sampled contourlet transform (NSCT) and by machine learning technique based on the random forest regression using a parameter adaptive pulse coupled neural network (PA-PCNN). The PA-PCNN adaptively estimates each of the free parameters of the pulse-coupled neural network (PCNN). The utilization of PA-PCNN considers neighborhood pixel variances and aids in maintaining object details without fuzziness or distortions. The proposed method restores the edges and boundaries of the objects effectively as PA-PCNN aids in maintaining the perceptually similar attributes of the saliency maps. The results of this study are evaluated using three widely used datasets for detecting salient objects, which show the potential of the proposed system to precisely locate the salient objects in various imaging circumstances like complex background images, images with multiple objects, etc. The quantitative and qualitative experimental results validate a substantial advancement in various evaluation parameters for salient object detection with better boundary preservation of objects.

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Data availibility statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, 440010, India

    Bhagyashree V. Lad

  2. Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Warangal, Telangana, 506004, India

    Mohammad Farukh Hashmi

  3. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, 440010, India

    Avinash G. Keskar

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  1. Bhagyashree V. Lad
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  2. Mohammad Farukh Hashmi
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  3. Avinash G. Keskar
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Correspondence to Bhagyashree V. Lad.

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Lad, B.V., Hashmi, M.F. & Keskar, A.G. Parameter adaptive pulse coupled neural network-based saliency map fusion strategy for salient object detection. Neural Comput & Applic 35, 15743–15757 (2023). https://doi.org/10.1007/s00521-023-08579-w

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