Abstract
Saliency computational model with active environment perception can be useful for many applications including image retrieval, object recognition, and image segmentation. Previous work on bottom-up saliency computation typically relies on hand-crafted low-level image features. However, the adaptation of saliency computational model towards different kinds of scenes remains a challenge. For a low-level image feature, it can contribute greatly on some images but may be detrimental for saliency computation on other images. In this work, a novel data driven approach is proposed to adaptively select proper features for different kinds of images. This method exploits low-level features containing the most distinguishable salient information per image. Then the image saliency can be calculated based on the adaptive weight selection scheme. A large number of experiments are conducted on the MSRA database to compare the performance of the proposed method with the state-of-the-art saliency computational models.
Highlights
Saliency computational model with active environment perception can be useful for many applications including image retrieval, object recognition, and image segmentation
A large number of experiments are conducted on the MSRA database to compare the performance of the proposed method with the state-of-the-art saliency computational models
The generated saliency map can highlight the salient object in different images even containing complex background
Summary
Saliency computational model with active environment perception can be useful for many applications including image retrieval, object recognition, and image segmentation. Most of the existing saliency computational models are based on the bottom-up mechanism because the visual attention is generally driven by the low-level stimulus such as edge [4], color [5, 6], orientation [7], and symmetry [8]. Most of the existing saliency computational models face great difficulties in adaptively selecting low-level image features towards different images. Aiming to address this problem, this paper puts forward an adaptive fusion scheme towards low-level image features for saliency detection. The visual perceptual object can be detected by the adaptive weight selection towards these low-level image features This method can retain the most significant lowlevel image features for saliency computation. Fusion map Center prior saliency map Figure 1: Flowchart of the proposed saliency computational model
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