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A. Borji, D. N. Sihite, L. Itti, Salient Object Detection: A Benchmark, In: Proc. European Conference on Computer Vision (ECCV), Florence, Italy (LNCS 7573), pp. 414-429, Oct 2012. [2012 acceptance rate: 25.0%]
Abstract: Several salient object detection approaches have been published which have been assessed using different evaluation scores and datasets resulting in discrepancy in model comparison. This calls for a methodological framework to compare existing models and evaluate their pros and cons. We analyze benchmark datasets and scoring techniques and, for the first time, provide a quantitative comparison of 35 state-of-the-art saliency detection models. We find that some models perform consistently better than the others. Saliency models that intend to predict eye fixations perform lower on segmentation datasets compared to salient object detection algorithms. Further, we propose combined models which show that integration of the few best models outperforms all models over other datasets. By analyzing the consistency among the best models and among humans for each scene, we identify the scenes where models or humans fail to detect the most salient object. We highlight the current issues and propose future research directions.
Themes: Model of Bottom-Up Saliency-Based Visual Attention, Model of Top-Down Attentional Modulation, Computational Modeling, Computer Vision
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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