Saliency detection based on short-term sparse representation

Representation and measurement are two important issues for saliency models. Different with previous works that learnt sparse features from large scale natural statistics, we propose to learn features from short-term statistics of single images. For saliency measurement, we define background firing rate (BFR) for each sparse feature, and then we propose to use feature activation rate (FAR) to measure the bottom-up visual saliency. The proposed FAR measure is biological plausible and easy to compute, also with satisfied performance. Experiments on human eye fixations and psychological patterns demonstrate the effectiveness and robustness of our proposed method.

• Type: Journal
• Year: 2010
• Where: ICIP
• Authors: Xiaoshuai Sun, Hongxun Yao, Rongrong Ji, Pengfei Xu, Xianming Liu, Shaohui Liu
• Keywords: Bottom-up Visual Saliency, Feature Activation Rate, ICIP 2010, Image Processing, Scale Natural Statistics