Two-stage salient object identification and segmentation based on irregularity

Saliency extraction is a technique inspired by the human approach in processing a selected portion of the visual information received. This feature in the human visual system helps reduce the processing the brain needs to extract important information and neglect general and unimportant information. This paper presents a novel approach to identifying the saliency of regions in a scene from which objects likely to be salient can be extracted. The proposed approach uses two stages, namely, local saliency identification (LSI) and global saliency identification (GSI) and uses irregularity as the saliency measure in both stages. Local saliency uses the structure of the object to determine saliency while global saliency identifies the saliency of the region based on the contrast in relation to the entire background. An object is considered to be salient if it satisfies both local and global criteria. In this work, the key challenges and limitations of existing methods, such as the sensitivity to texture and noise, the need to manually define certain parameters, and the need to have pre-knowledge of the nature of the image, were considered and appropriate solutions have been suggested. The proposed algorithm was tested on a set of 1000 images selected from MSRA saliency identification standard dataset and benchmarked with state-of-the-art approaches. The results obtained showed very good efficiency and this is evident from the evaluation values obtained from the used evaluation method, e. g. the value of the F-measure, reached 96.5 per cent in some cases. The limitation of the approach was with complex objects which themselves comprising more than one important region such as an image of a person. This will be discussed thoroughly in the result section.

Automated summary

This paper introduces a saliency extraction technique inspired by the human visual system, which identifies salient regions in a scene through local saliency identification and global saliency identification stages. The proposed method has several advantages over existing methods and demonstrates high efficiency in testing.