The saliency detection of RGB images has gained popularity in recent years. Saliency detection aims to quickly and accurately find salient objects in an image. Existing saliency detection methods utilize center prior

boundary prior

and color contrast methods. The center prior method is based on the assumption that salient objects are in the center of the image. The boundary prior method is based on the assumption that the image boundary is in the background instead of the foreground. The color contrast method is based on the principle that objects and their surroundings have varying contrast. Diffusion-based compactness method on the basis of the principle that salient objects typically have compact spatial distributions

whereas background regions have a wider distribution over the entire image. Fusion is required given that each method has its own advantages and weaknesses. In photography

important objects are placed at photographic composition intersections or arranged along photographic composition lines. Inspired by this photographic composition rule

we proposed saliency detection method based on the center rectangle composition prior

which assumes that the salient object is near the central rectangular composition lines. To supplement the weakness caused by assumptions- for example

the salient object is not near the center rectangular composition lines- we fuse compactness method to correct its result from spatial distribution. The compactness method calculates the spatial variance of each superpixel

as well as computes the spatial distance of the superpixels from the photographic composition intersection or the center of image. The choice of photographic composition intersection lies on cover range of the four intersections in initial saliency map. First

the image was converted into superpixels and a graph model was described. Then

we set the superpixels in the central rectangle composition lines as query nodes

extracted their color feature to rank with all the other regions by manifold ranking

and then generated the center rectangle composition lines saliency map. Third

we computed the spatial variance of each superpixel to generate compactness relation distribution saliency map. Fourth

we found all the photographic composition as intersections

which are salient in a center rectangle composition lines saliency map. Finally

we calculated and combined the spatial distance of superpixels from them to generate photographic composition intersection saliency map. If no singular data are found

the center point of image was selected. Finally

the final saliency map was obtained by fusing the center rectangle composition lines saliency map

center rectangle composition intersection saliency map

and compactness relationship distribution saliency map. Experiments were performed to compare the performances of nine different methods using MSRA-1000

CSSD

ECSSD

and THUS-10000 databases. Results showed that our final saliency maps were closer to the ground truth and our method had better precision-recall curve and higher F-measure value. Our method had an average running time of 0.673 s for one image

thus achieving the real-time requirement. Moreover

experiments showed that although Monica can produce the original saliency results generated by other methods

all of them were greatly improved to a similar accuracy level after optimization by center rectangle composition prior calculation. Our proposed method outperforms state-of-the-art methods given its effectiveness

robustness

and real-time application regardless of the complexity of the image or size of the salient object. These advantages are conferred by center rectangle composition prior and fusion of center rectangle composition lines prior

center rectangle composition intersection prior

and the compactness relationship.