An efficient visual saliency detection model based on Ripplet transform

Even though there have been great advancements in computer vision tasks, the development of human visual attention models is still not well investigated. In day-to-day life, one can find ample applications of saliency detection in image and video processing. This paper presents an efficient visual saliency detection model based on Ripplet transform, which aims at detecting the salient region and achieving higher Receiver Operating Characteristics (ROC). Initially the feature maps are obtained from Ripplet transform in different scales and different directions of the image. The global and local saliency maps are computed based on the global probability density distribution and feature distribution of local areas, which are combined together to get the final saliency map. Ripplet-transform-based visual saliency detection is the novel approach carried out in this paper. Experimental results indicate that the proposed method based on Ripplet transformation can give excellent performance in terms of precision, recall, F measure and Mean Absolute Error (MAE), and is compared with 10 state-of-the-art methods on five benchmark datasets.

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