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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Modified ResNet50 model and semantic segmentation based image co-saliency detection

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pp. 1035–1042Vol. 44Issue 6September 2023DOI: 10.47974/JIOS-1331XML
Published Online:
11 Nov 2023
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1331
Pages:
1035–1042

Abstract

Co-salient object identification is a new and emerging branch of the visual saliency detection technique that tries to find salient pattern appearing in several image groups. The proposed work has the potential to benefit a wide variety of important applications including the detection of objects of interest, more robust object recognition and animation synthesis, handling input image query, 3D object reconstruction, object co-segmentation etc. To build modified ResNet50 model, the hyperparameters are adjusted in the current work to increase accuracy while minimizing loss. The modified network is trained on HOG features to mine more significant features along with their corresponding ground truth images. For a more streamlined outcome, the proposed system was built using the SGDM optimizer. During testing among the relevant and irrelevant image the network generates appropriate co-saliency map of relevant images. Integrating the associated and prominent characteristics of the image yields the appropriate ground truth for each image. The proposed method reports better F1 value 98.7% and MAE score 0.089 value when compared with SOTA model.

Keywords

Subject Classifications

68T07

References

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