The human visual system can acquire regions of interest for different scenes based on the visual attention mechanism.Each image contains one or more salient objects.Saliency detection involves imitating the visual attention mechanism to obtain important information in an image

thereby improving the efficiency and accuracy of image processing.Saliency detection methods can be used not only in detecting a target object

but also in image annotation and retrieval

object recognition

image clipping

image segmentation

image compression

and other fields.Saliency detection is a research hot spot in computer vision.Although existing significant detection methods have achieved good results

several problems remain

such as the blurring of significant boundaries due to foreground and background noises.Therefore

the accuracy of saliency detection should be improved.Saliency detection methods based on pixels or regions

such as super pixels

can effectively describe the features of salient regions.However

these pixels or regions exist alone and have no real object significance;that is

complete descriptions of objects are lacking.Objectness detection involves obtaining object information by sliding windows.We propose a saliency detection algorithm via object enhancement and sparse reconstruction (OESR) to introduce object descriptions while preserving the effective description of salient features to solve the problems of fuzzy boundaries and improve the accuracy of image saliency detection.The objectness detection method is not used to directly access windows as the final salient objects.We consider window information as an object description to enhance the effectiveness of salient features. The input image is segmented by super pixels

and several super pixel regions are obtained.A central weighted color spatial distribution model is adopted.The model is based on the idea that when a wide range of colors exist

these colors are less likely to belong to a salient region.This model utilizes the color information of an image

but the method is based on pixels.Consequently

the final result lacks structured information.We calculate the color space distribution feature on super pixels to introduce structured information.First

from the foreground point of view

the Gauss mixture model is used to model all colors

and the probability of each pixel corresponding to the C color component is calculated.The probability of each super pixel corresponding to the C color component is calculated through the probability of pixels within a super pixel.The color spatial distribution based on super pixels is calculated through its probability and location information.We use the super pixel color spatial distribution map as the foreground saliency map.Second

from the background point of view

we introduce a sparse reconstruction error based on the idea of contrast to describe the feature difference between a super pixel and its surrounding super pixels.We construct the background template by using the super pixel features of the image boundaries.We conduct pretreatment on the template using the -means clustering algorithm for a combined treatment on the boundary of super pixels to obtain representative boundary features.Super pixels with similar features are merged in each direction;thus

good background templates are obtained.The optimized background template is used as a sparse representation dictionary to compute sparse reconstruction errors.The reconstruction error of a super pixel is corrected in its 8-neighbor to solve the region discontinuity caused by the oversegmentation of an image.After correction

the saliency region becomes smooth

and the sparse reconstruction error is set as the salient value to obtain the background difference map.Finally

from the object point of view

the target enhancement coefficient is calculated using the objectness detection method.We use a fast target detection method to obtain a certain number of proposed windows with various scales.Each window assigns an object score based on the possibility of containing the object.If a pixel belongs to a salient region

then the more target windows that contain this pixel

the higher object scores it will have

and the greater its significance.The target enhancement coefficient is calculated from the object scores of the proposed windows.The foreground saliency map and the background difference map are fused

with the target enhancement coefficient as the guide.We obtain a high-contrast salient map with a significant foreground and a suppressed background. The proposed algorithm is compared with 12 methods on two public data sets (i.e.

MSRA10k and VOC2007).The precision

recall

F-measure

and mean absolute error (MAE) are evaluated on the two data sets.The visual contrast of the experiment indicates that the salient objects detected using OESR are complete and accurate

and the method can also effectively deal with images with complex background.OESR uses the target enhancement coefficient in the salient map fusion step.The final salient region has high brightness

and background suppression works efficiently.The P-R curve

average precision

average recall

and average F-measure value indicate that OESR exhibits certain advantages in the three evaluation indexes.Compared with other methods

our method has improved recall of 4.1% on the MSRA10k data set.In the VOC2007 data set

the recall of our method improves by 18.5%

and F-measure improves by 3.1%.Recall represents the recall situation of images to be detected.An improvement in recall shows that the color features and sparse reconstruction features can describe the salient features effectively

and the introduction of object information ensures the integrity of salient regions.The result of the MAE evaluation index also reflects the advantage of OESR in terms of overall performance. A new saliency detection method is proposed in this study.The method uses color spatial distribution and sparse reconstruction error to produce a saliency map.Object enhancement coefficients are adopted in the combination of the two maps to improve the accuracy of saliency maps.Experimental results show that the algorithm can detect accurate salient regions

which agree with human vision characteristics.This method is suitable for saliency detection

target segmentation

and image annotation based on saliency analysis.