Objective

2

Saliency object detection with development of human visual attention mechanism has been widely studied by computer vision researchers. Visual significance is an important mechanism of human visual system. It simulates the human visual attention mechanism

extracts the most interesting areas of the scene quickly and accurately

and ignores redundant information. Saliency object detection has been widely used in image compression

segmentation

redirection

video coding

target detection

recognition

and many other tasks. Although numerous significant target detection methods are available

problems remain. For example

the detection results look well when the background is simple

but when the background is complex

the results may have some uncertainty as regards the environment

cluttered background in the area around the target

or influence of selection on the significant target detection method. The problem of cluttered background and inaccurate detection area often occurs when the salient object detection method generates significant graphs. To solve these problems

saliency object detection method is proposed based on complex domain. The complex domain combines frequency

spatial

and wavelet domains; takes advantage of the complex domain to combine the advantages on three domains; and suppresses the background to obtain an accurate and clear salient target area.

Method

2

Environmental conditions are one of the key factors that influence saliency object detection; for example

weak light or foggy days can cause unclear images and lead to poor results of significant target detection. Multi-scale retinex is an image enhancement algorithm based on color theory. By introducing multi-scale retinex algorithm

the image restoration is realized by linear weighting in the process of dynamically scaling a picture. First

multi-scale retinex enhancement algorithm is used to preliminarily process the original image in spatial domain and exclude environmental impacts. After image processing

the brightness becomes more appropriate to the real scene brightness

and the foreground and background contrast is also significantly improved. In addition to the environmental impact

the background areas of the non-significant target often occupy most of the image space in the saliency object detection images. These background areas increase the error detection problem and reduce the accuracy rate. Experiments found that most background areas are the sky

trees

grasslands

and buildings

which are beyond the scope of this study. The characteristics of the background areas with repeatability can be suppressed by hyper-complex Fourier transform. Then

undirected graph is established and node features on the images are extracted preliminarily. The hyper-complex Fourier transform in the frequency domain is reconstructed to acquire the smoothing amplitude spectrum

phase spectrum

and Euler spectrum. Then

background suppression graphs are obtained through the smoothness of multi-scale Gaussian kernel. At the same time

the multi-level feature of wavelet transform in the wavelet domain is utilized to extract multiple features in terms of images

and the saliency graph of multiple features is calculated. The saliency graph effectively preserves the details of the image because of the unique localization characteristics of the wavelet domain. Finally

the proposed adaptive threshold selection method is used to fuse the background suppression diagram with the saliency graph of multiple features and the final saliency graph is selected and obtained. The final saliency figure suppresses the background while preserving the details of the image.

Result

2

To make the experimental effect persuasive

saliency object detection experiments in the standard test dataset images MSRA10K and THUR15k are conducted. MSRA10K datasets consist of 10 000 images of hand-annotated and accurate to pixel-level salient target annotations

including images of natural scenery

biology

architecture

and transportation. THUR15K datasets consist of 15 000 web images with five keywords

namely

butterflies

airplanes

giraffes

cups

and dogs

representing significant targets with pixel precision as the former datasets. The two datasets are public standard image databases and are widely used in salient target detection and image segmentation. A total of 300 background-complex pictures are selected from each dataset

under the same experimental conditions

and compared with six popular significant target detection methods. Results show that the problems presented by our method had a good solution. Even in a complex environment

the accuracy and recall rate of the algorithm are higher than those of state-of-the-art contrast algorithms. In MSRA10k datasets

the mean absolute error (MAE) value is 0.106; in THUR15K datasets

the mean absolute error value was reduced to 0.068

and the average structure (s) measure value was 0.844 9. The result of the MAE evaluation reflects the advantage of a saliency object detection method based on complex domain in terms of overall performance

and the s-measure indicates that the detected target is highly similar to the structure of the target of the ground truth graph.

Conclusion

2

Saliency object detection is a promising preprocessing operation in image processing and analysis. In this study

a new saliency object detection method based on complex domain is proposed. Multi-scale retinex algorithm in spatial domain can be used for pretreatment of images; it enhances contrast and prevents images from being affected by environmental factors. Hyper-complex Fourier transform in the frequency domain can suppress complex repetitive background regions

and the significant target detection method in the wavelet domain can completely describe the details of the target. Moreover

the proposed algorithm integrates the advantages of multiple domains and improves the accuracy while suppressing background clutter. Thus

the proposed algorithm is suitable for detecting significant target images

such as natural scenery

biology

architecture

and transportation. To improve the algorithm speed

our next research project aims to reduce the complexity of the algorithm by using the influence of wavelet transform function on time complexity.