Manuscript received September 25, 2023; revised December 14, 2023; accepted December 26, 2023; published June 20, 2024

Abstract—Salient Object Detection (SOD) can mimic the human vision system by using algorithms that simulate the way how the eye detects and processes visual information. It focuses mainly on the visually distinctive parts of an image, similar to how the human brain processes visual information. The approach proposed in this study is an ensemble approach that incorporates classification algorithm, foreground connectivity and prior calculations. It involves a series of preprocessing, feature generation, selection, training, and prediction using random forest to identify and extract salient objects in an image as a first step. Next, an object proposals map is created for the foreground object. Subsequently, a fusion map is generated using boundary, global, and local contrast priors. In the feature generation step, different edge filters are implemented as the saliency score at edges will be high; additionally, with the use of Gabor’s filter the texture-based features are calculated. The Boruta feature selection algorithm is then used to identify the most appropriate and discriminative features, which helps to reduce the computational time required for feature selection. Ultimately, the initial map obtained from the random forest, along with the fusion saliency maps based on foreground connectivity and prior calculations, is merged to produce a saliency map. This map is then refined using post-processing techniques to acquire the final saliency map. The approach we propose surpasses the performance of 17 cutting-edge techniques across three benchmark datasets, showcasing superior results in terms of precision, recall, and f-measure. The proposed method performs well even on the DUT-OMRON dataset, known for its multiple salient objects and complex backgrounds, achieving a Mean Absolute Error (MAE) value of 0.113. The method also demonstrates high recall values (0.862, 0.923, 0.849 for ECSSD, MSRA-B and DUT-OMRON datasets, respectively) across all datasets, further establishing its suitability for salient object detection.

Keywords—computer vision, salient object detection, random forest, classification, visual attention, visual saliency, video surveillance

Cite: Gayathri Dhara and Ravi Kant Kumar, "Enhancing Salient Object Detection with Supervised Learning and Multi-prior Integration," Journal of Image and Graphics, Vol. 12, No. 2, pp. 186-198, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.