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.
In @cite_14 , also proposed to retrieve similar images for saliency detection. However, our approach is different from theirs in three aspects. First, we address saliency detection based on region proposals, which contain a large amount of shape and boundary information of salient regions and keep the consistency of the whole object or part of it. Second, our approach uses a more discriminative SVM, instead of distance-based classification, to better predict the saliency values of object proposals. Our annotation database consists of 50,000 images, which is large enough to contain similar examples for most test images. Third, unlike @cite_14 which relies purely on a retrieved list and thus potentially suffers from retrieval errors for uncommon objects, we use internal saliency cues with external high-level retrieved information to leverage the best out of both schemes. Our method combines the supervised and unsupervised algorithms, considering high-level object concepts and low-level contrast simultaneously, and thus can uniformly highlight the whole salient region with explicit object boundaries and achieves better performance on the PR curves.
@cite_14
2107363596
We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image; secondly, we build a simple classifier and we use it to generate saliency maps. Finally, we refine the maps and we extract thumbnails. We show that in spite of its simplicity, our framework outperforms state-of-the-art approaches. Another advantage is its ability to deal with visual pop-up and application task-driven saliency, if appropriately annotated images are available.