The detection of region of interest (ROI) is the a key technique in image processing. Human visual system focus on a few objects in a complicated natural environment. These objects are called region of interest. The model of region of interest detection can simulate the human visual system and accurately compute the saliency area in image processing. This model can improve the efficiency of computer processing and reduce calculation complexity. Thus

the detection of region of interest is of great significance. A bottom-top ROI detection method is proposed based on low level image cues combined with middle level cues. First

the middle level coarse saliency region is obtained via a convex hull of corner detected by boosting Harris and superpixels clustering. The original image is then transformed from RGB color space to CIELab color space

and the difference of Gaussian filter method is presented to obtain the low level coarse saliency map. Eventually

the saliency map of the initial image is obtained by fusing the two coarse saliency maps. Extensive experiments on the large data set coming from Microsoft Asian research institution show that our method performs better than state-of-the-art algorithms. For fair evaluation

the results obtained via the five methods are based on the source codes provided by the authors. Both a subjective and objective evaluations of the proposed method compared with the other five methods are presented. The subjective comparison illustrates that our method provides accurate location

well-defined boundaries

uniform highlight

and full resolution saliency map. Moreover

the objective comparison via precision-recall curve shows that our method performs well in precision. Experiments show that this method can clearly highlight the whole salient object via reduced degrees of saliency levels

significantly alleviate the influence of false positive pixels

and obtain well-defined boundaries. In conclusion

our method can be generally exploited as an image preprocessing method.