Can Image Retrieval help Visual Saliency Detection?

TL;DR

This paper introduces a novel approach combining image retrieval and machine learning to improve visual saliency detection by integrating external and internal saliency maps, showing promising results on challenging datasets.

Contribution

The paper presents a new framework that leverages image retrieval and SVM-based prediction to enhance saliency detection, integrating external and internal cues for better accuracy.

Findings

Outperforms state-of-the-art methods on multiple datasets

Combining external and internal saliency maps improves detection accuracy

Effective use of bounding box annotations and low-level contrast information

Abstract

We propose a novel image retrieval framework for visual saliency detection using information about salient objects contained within bounding box annotations for similar images. For each test image, we train a customized SVM from similar example images to predict the saliency values of its object proposals and generate an external saliency map (ES) by aggregating the regional scores. To overcome limitations caused by the size of the training dataset, we also propose an internal optimization module which computes an internal saliency map (IS) by measuring the low-level contrast information of the test image. The two maps, ES and IS, have complementary properties so we take a weighted combination to further improve the detection performance. Experimental results on several challenging datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.