Abstract

Several salient ob ject detection approaches have been pub- lished 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 pre- dict eye fixations perform lower on segmentation datasets compared to salient ob ject detection algorithms. Further, we propose combined mod- els 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 ob ject. We highlight the current issues and propose future research directions.