Sentence examples for successful saliency models from inspiring English sources

Over the past years, many different successful saliency models have been proposed especially for image saliency prediction.

Inspired by the centre-surround receptive field design of neurons in the retina [ 18], several successful saliency models are based on comparison of centre-surround regions at each image location [ 4, 5, 10, 15, 19, 23, 36].

We conclude that fixations agree with saliency judgments, and classic bottom-up saliency models explain both.

We compared it with 2D saliency methods, mixed models, and stereoscopic 3D saliency models.

The projected saliency framework enabled to study the interaction of features in the feature activation process of computational saliency models.

Recently, two spatiotemporal saliency models were presented in [13, 14].

The final result combines the 2D saliency maps (from 2D saliency models usually using color contrast, intensity, or image texture) and the depth-saliency maps (DSM).

We demonstrate that a saliency model based on this better understanding of viewing behavioral biases and blind to any visual information outperforms well-established saliency models.

Thus we propose to build visual attention models specifically for face images through combining low-level saliency calculated by traditional saliency models with high-level facial features.

Yet, many dynamic saliency models follow a similar simple design and extract separate spatial and temporal saliency maps which are then integrated together to obtain the final saliency map.

In addition, our learning-based models outperform the state-of-the-art dynamic saliency models.