Visual saliency detection: From space to frequency

• Some frequency-based saliency models can be transformed into the space domain.
• The frequency-based model derived from the transformation of multiple Gabor filters outperforms most of competing models.
• The SWE-based scale competition is a better scheme for scale combination.
• ARI is a better saliency metric than other AUC-based metrics.

In this paper, a new saliency detection model is proposed based on a space-to-frequency transformation. Firstly, the equivalence of spatial filtering and spectral modulation is demonstrated to explain the intrinsic mechanism of typical frequency-based saliency models. Then a novel frequency-based saliency model is presented based on the Fourier Transformation of multiple spatial Gabor filters. Besides, a new saliency measurement is proposed to implement the competition between saliency maps at multiple scales and the fusion of color channels. In experiments, we use a set of typical psychological patterns and four popular human fixation datasets to test and evaluate the proposed model. In addition, a new energy-based criterion is proposed to evaluate the performance of our model and is compared with five traditional saliency metrics for validation. Experimental results show that our model outperforms most of the competing models in salient object detection and human fixation prediction.