Objective

2

Object segmentation from a single image is a fundamental step in many computer vision and digital image processing applications

such as image semantic recognition

medical image analysis

traffic control

weather forecasting

image searching

geological exploration

and scene understanding. Traditional image segmentation methods mainly use low-level semantic information of an image

which includes the color

texture

and shape of image pixels. This method is not ideal because it easily causes oversegmentation and overmerging in the actual segmentation of a complex scene. Obtaining semantic segmentation results is difficult. A novel approach that combines graph-based algorithm and t junction cues is proposed to improve the oversegmentation and merging of a single image object segmentation algorithm.

Method

2

On the basis of the Graph-based algorithm

t junction cues are introduced

which can guarantee accurate segmentation results to avoid the influence of color information on segmentation. First

L0 gradient minimization is applied to smooth the target image and eliminate artifacts caused by noise and texture detail. The L0 gradient minimization algorithm uses the number of non-zero gradients in the image to retain useful edge details in the image and removes useless edge parts. Thus

image smoothing is achieved. The L0 gradient minimization algorithm achieves edge-preserving smoothing without changing the artwork structure. The initial oversegmentation result of the smoothing image uses the Graph-based algorithm. The Graph-based algorithm considers the similarity between regions and the differences between regions and maintains image boundaries better. However

it cannot control the number of image blocks and the compact program. A region merging algorithm is applied to optimize the error segmentations by t junction cues.

Result

2

The proposed algorithms are compared with the Grabcut algorithm and the Graph-based algorithm under different scene types. Experimental results show that the Grabcut algorithm of artificial boundary and positioning is only a single object segmentation. The proposed algorithm has advantages in terms of robustness

time consumption

and boundary location accuracy over the Grabcut algorithm. The Graph-based method considers the similarities between and differences among regions. The image boundaries can be maintained well by segmenting the vertices in the graph. However

the defects of the Graph-based algorithm are obvious

such as class difference. Moreover

the number and the compactness of the image blocks are not controlled

and the segmentation effect depends strongly on the selected threshold. Oversegmentation and overmerging occur easily. The average rate of segmentation accuracy for several groups of different types of images reaches 91.16% given other algorithms. The image size of 800×600 pixels and the average time of 3.5 s increased slightly compared with other algorithms. The proposed algorithm is more time consuming than the Graph-based algorithm

but segmentation accuracy is greatly improved. The proposed algorithmcan generate concise and smooth segmentation boundaries and obtain segmentation results with some semantics.

Conclusion

2

This study proposes the object segmentation algorithm of a single image on the basis of t junction cues. A comparison of the proposed algorithm and the traditional graph-based and improved Grabcut algorithms shows the effectiveness of the proposed algorithm. Moreover

the proposed algorithm can effectively solve the problem of oversegmentation and overmerging

and semantic object segmentation results can be achieved. Experimental results show the effectiveness of the proposed algorithm.