Computing salient information within digital images has been the focus of considerable research interest and has been applied in many applications

e.g.

object detection

image segmentation

visual tracking

and content-based image retrieval. In most models

image saliency has been regarded as local regions that can be easily differentiated from their surroundings. However

the main problem existing in the models is that salient regions have limited ability to locate a complete object. A method that combines regions and edges is proposed to solve the problem. For regions

an isophote-based operator is designed to detect potential structures. According to the findings from neurobiology and psychophysics

salient information can be defined as regions that popped out from their surroundings based on certain feature channels

such as color

intensity

and orientation. Thus

the isophote-based operator is employed to extract salient regions from three kinds of features

namely

color

intensity

and orientation. The operator mainly establishes a consistent measurement for various feature channels

which easily integrates multi-feature saliency. For edges

a global saliency detector is adopted with the multi-scale Beltrami filter. The multi-scale Beltrami filter could enhance edge information within images while blurring detailed information of interior regions. By processing through the multi-scale Beltrami filter

global image saliency detectors could locate salient edges easily. Finally

the salient regions and edges are integrated by a linear method directly. The database used in this study includes 1 000 images from a variety of sources and has ground truths in the form of accurate human-marked labels for saliency information. Two kinds of measurements are adopted in the experiment

namely

segmentation by fixed thresholding and segmentation by adaptive thresholding. In both measurements

the proposed method exhibits impressive performance compared with nine other well-known methods

with 0.92 rate of receiver operating characteristic area in segmentation by adaptive thresholding and 0.5905

0.6554

0.7470 rate of average precision

recall

F-measure in segmentation by adaptive thresholding. Image saliency is one of the key features for many applications. This study proposes a novel method that combines region and edge information to locate complete salient objects. As shown in the experiment

the proposed method has good applicability and robustness.