Objective

2

Saliency detection is a technique that uses algorithms to simulate human visual characteristics. It aims to identify the most conspicuous objects or regions in an image and is used as a first step in image analysis and synthesis

allowing priority to be given to the allocation of computational resources in subsequent processing. The technique has been widely used in several visual applications

such as image segmentation of regions of interest

object recognition

image adaptive compression

and image retrieval. In most traditional methods

the basic unit of saliency detection is formed by image oversegmentation on the basis of regular regions

which are usually improved on

n

×

n

square blocks. Final saliency maps consist of these regions with their saliency scores

which result in the boundary block effect of the final saliency map. The performance of these models relies on whether the segmentation results fit the boundary of the salient object and the accuracy of feature extraction. By using this method

an improved effect can be obtained on the salient target with relatively regular structure and texture. However

in the real world

significant objects and backgrounds are often characterized by complex textures and irregular structure. These approaches cannot produce satisfactory results when images have complex textures

which yield low accuracy. To deal with the limitations of the past algorithms

we propose an algorithm for salient object detection based on irregular superpixels. This algorithm can consider the information of the structure and color features of the object and is closer to the object boundary to a certain extent

thus increasing the precision and recall rate.

Method

2

In the algorithm

the images to be inputted are first preprocessed by bilateral filtering and mean-shift in to reduce the scattered dots in the picture. Then in the RGB(red-green-blue) color space

the K-means algorithm is used to quantize the color of the image

and the color values of the cluster center and the cluster center are obtained and saved

in order to speed up the subsequent calculations. Then

according to the connected domain of the same color label

irregular pixel clusters are formed

in the meantime

set the center of the connected domain to the center of the location of the cluster

set the color corresponding to the color label of the connected domain to the color of the cluster. Next

for the contrast prior

the saliency scores of the image pixel cluster is determined by the color statistic information of the input image. In particular

the saliency scores of a pixel cluster is defined by its color contrast with all other pixel clusters in the image

the size of the pixel cluster and the probability of the corresponding color appearing in the picture. For the central prior graph

the center of the significant target is first estimated by the target coarse positioning method. Then

on the basis of the distance between clusters and the center

the saliency scores of each pixel cluster can be calculated; thus

the central prior graph can be formed. The contrast prior graph is then combined with the central prior graph to obtain an initial saliency map. Lastly

to make the salient map highlight the significant target prominently

a graph model and morphological changes are introduced in saliency detection due to their outstanding performance in image segmentation tasks. In this manner

the final saliency map is obtained.

Result

2

To test the recognition effect of the proposed algorithm

we compare our model with five excellent saliency models on two public datasets

namely

DUT-OMRON(Dalian University of Technology and OMRON Corporation) and Microsoft Research Asia(MSRA) salient object database. The quantitative evaluation metrics contain F-measure and precision-recall(PR) curves. We provide several saliency maps of each method for comparison. Experiment results show that the algorithm proposed in this study has a greater performance improvement compared with the previous algorithms; it also has a better visual effect in the MSRA and DUT-OMRON datasets. The saliency maps show that our model can produce refined results. Compared with the detection results in frequency-tuned salient region detection(FT)

luminance contrast(LC)

histogram based contrast(HC)

region based contrast(RC)

and minimum barrier salient object detection(MB) in MSRA

the F-measure (higher is better) increases by 47.37%

61.29%

31.05%

2.73%

and 5.54%

respectively. Compared with DUT-OMRON

the F-measure increases by 75.40%

92.10%

63.50%

8.83%

and 16.34%

respectively. Comparative experiments demonstrate that the fusion algorithm improves saliency detection. In addition

a series of comparative experiments in MSRA are conducted to show the preponderance of our algorithm.

Conclusion

2

In this study

a saliency recognition algorithm based on irregular pixel blocks is proposed. The algorithm is divided into three parts: irregular pixel blocks

which are constructed by using the color information of images; initial saliency graph

which is obtained by fusing contrast prior and center prior; The final saliency map is obtained by improving the initial saliency map using a graph model. Experimental results show that our model improves recognition performance and outperforms several best performing saliency approaches.