Automatic Salient Object Detection for Panoramic Images Using Region Growing and Fixation Prediction Model

TL;DR

This paper introduces a novel bottom-up framework for salient object detection in panoramic images, combining region growing, fixation prediction, and geodesic refinement, validated on a new high-quality dataset.

Contribution

The paper presents a new saliency detection method specifically designed for panoramic images, integrating spatial density, fixation prediction, and geodesic refinement, along with a new dataset for evaluation.

Findings

Effective detection of salient objects in panoramic images

Outperforms existing methods on the SalPan dataset

Demonstrates robustness and accuracy in diverse panoramic scenes

Abstract

Almost all previous works on saliency detection have been dedicated to conventional images, however, with the outbreak of panoramic images due to the rapid development of VR or AR technology, it is becoming more challenging, meanwhile valuable for extracting salient contents in panoramic images. In this paper, we propose a novel bottom-up salient object detection framework for panoramic images. First, we employ a spatial density estimation method to roughly extract object proposal regions, with the help of region growing algorithm. Meanwhile, an eye fixation model is utilized to predict visually attractive parts in the image from the perspective of the human visual search mechanism. Then, the previous results are combined by the maxima normalization to get the coarse saliency map. Finally, a refinement step based on geodesic distance is utilized for post-processing to derive the final saliency map. To fairly evaluate the performance of the proposed approach, we propose a high-quality dataset of panoramic images (SalPan). Extensive evaluations demonstrate the effectiveness of our proposed method on panoramic images and the superiority of the proposed method against other methods.