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收稿：2019-06-06，
修回：2019-8-29，
录用：2019-9-5，
纸质出版：2020-02-16
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目的
2
基于超像素分割的显著物体检测模型在很多公开数据集上表现优异，但在实际场景应用时，超像素分割的数量和大小难以自适应图像和目标大小的变化，从而使性能下降，且分割过多会耗时过大。为解决这一问题，本文提出基于布尔图和灰度稀缺性的小目标显著性检测方法。
方法
2
利用布尔图的思想，提取图像中较为突出的闭合区域，根据闭合区域的大小赋予其显著值，形成一幅显著图；利用灰度稀缺性，为图像中的稀缺灰度值赋予高显著值，抑制烟雾、云、光照渐晕等渐变背景，生成另一幅显著图；将两幅显著图融合，得到具有全分辨率、目标突出且轮廓清晰的显著图。
结果
2
在3个数据集上与14种显著性模型进行对比，本文算法生成的显著图能有效抑制背景，并检测出多个小目标。其中，在复杂背景数据集上，本文算法具有最高的F值（F-measure）和最小的MAE（mean absolute error）值，AUC（area under ROC curve）值仅次于DRFI（discriminative regional feature integration）和ASNet（attentive saliency network）模型，AUC和F-measure值比BMS（Boolean map based saliency）模型分别提高了1.9%和6.9%，MAE值降低了1.8%；在SO200数据集上，本文算法的F-measure值最高，MAE值仅次于ASNet，F-measure值比BMS模型提高了3.8%，MAE值降低了2%；在SED2数据集上，本文算法也优于6种传统模型。在运行时间方面，本文算法具有明显优势，处理400×300像素的图像时，帧频可达12帧/s。
结论
2
本文算法具有良好的适应性和鲁棒性，对于复杂背景下的小目标具有良好的显著性检测效果。
Objective
2
The task of salient object detection is to detect the most salient and attention-attracting regions in an image. It can be divided depending on its usage into two different types:predicting human fixations and detecting salient objects. Unlike detecting large-size objects from images with simple backgrounds and high center bias
the detection of small targets under complex backgrounds remains challenging. The main characteristics are the diverse resolution of input images
complicated backgrounds
and scenes with multiple small targets. Moreover
the location of objects under this scenario lacks prior information. To cope with these challenges
the salient object detection models must be able to detect salient objects quickly and accurately in an image without losing the information of the original image and the target and must be able to maintain stable performance when processing images with different sizes. In recent years
superpixel-based salient object detection methods have performed well on several public benchmark datasets. However
the number and size of superpixel can hardly self-adapt to the variation of image resolution and target size when transferred to realistic applications
resulting in reduced performance. Excessive segmentation of superpixels also results in high time consumption. Therefore
superpixel-based methods are unsuitable for salient object detection under complex backgrounds. Despite its shortcomings in suppressing cluttered backgrounds
methods based on global color contrast can uniformly highlight salient objects in an image. In addition
this type of method is computationally efficient compared with most superpixel-based methods. A newly proposed Boolean map-based saliency method has been known for its simplicity
high performance
and high computational efficiency. This method utilizes the Gestalt principle of foreground-background segregation to compute saliency maps from Boolean maps. The advantage of this method is that the calculation of the saliency map is independent of the size of the image or the target; thus
it can maintain stable performance on input images with different resolutions. However
the results remain less than ideal when dealing with images with a complex background
especially those with multiple small targets. Considering these problems
this paper proposes a novel bottom-up salient object detection method that combines the advantages of Boolean map and grayscale rarity.
Method
2
First
the input RGB image is converted into a grayscale image. Second
a set of Boolean maps is obtained by binarizing the grayscale image with equally spaced thresholds. The salient surrounded regions are extracted from the Boolean map. The saliency value of each salient region is assigned on the basis of its area. Third
the grayscale image is quantized at different levels
and its histogram is calculated. Afterward
the less frequent grayscale value in the histogram will be assigned a high saliency value to suppress several typical backgrounds
such as smog
cloud
and light vignetting. Finally
Boolean map and grayscale rarity saliencies are merged to obtain a final saliency map with full resolution
highlighted salient object
and clear contour. In the second step
instead of directly using the area of the salient region as the saliency value
the logarithmic value of the area is used to expand the difference between cluttered backgrounds and real salient objects. This strategy can efficiently suppress cluttered backgrounds
such as grass
car trail
and rock on the ground
and it can highlight the large salient object without excessively suppressing the small one. In the third step
when assigning saliency value to an individual pixel
not only the grayscale rarity but also the quantization coefficient is considered. The more the grayscale image is compressed
the greater the weight of the saliency value of its corresponding quantization level. In the final step
a simple linear multiplication strategy is used to fuse two different saliency maps.
Result
2
The experiment is divided into two parts:qualitative analysis and quantitative evaluation. In the first part
the stability and time consumption of our method and other classic methods are analyzed through computation on five images with multiple targets. These five images are downsampled from the same image; thus
they vary in resolution but are identical in content. We verify that most superpixel-based methods can hardly maintain stability when handling images with different resolutions. Several methods are good in large images
whereas others specialize in small images. In addition to instability
the time consumption of several methods on large-size images is unacceptable. Combined with time consumption and stability
the models that can maintain stability and have fast run speed are mainly pixel-based models
including Itti
LC (local contrast)
HC(histogram-based contrasty)
and our method. In the second part
all methods are quantitatively evaluated on three different datasets:the complex-background images
which are annotated by ourselves
the SED2 dataset
and the small-object images
which are from DUT-OMRON
ECSSD
ImgSal
and MSRA10K. First
our method obtains the highest F-measure value and smallest MAE (mean absolute error) score over the complex-background images and is only slightly lower than the DRFI (discriminative regional feature integration) and ASNet (attentive saliency network) method in terms of AUC (area under ROC curve) value. The AUC
F-measure
and MAE scores of our method are 0.910 2
0.700 2
and 0.045 8
respectively. Second
our method outperforms six traditional methods in the SED2 dataset. Furthermore
on the small-object images from public datasets
the performance of our algorithm is second only to the ASNet method and has the highest F-measure value. On the basis of the visual comparison of different saliency maps
1) superpixel-based methods tend to ignore the small objects
even though these objects have the same feature as the large objects; 2) methods based on color contrast can detect large and small objects in the image
but they also highlight the background; 3) our method can efficiently suppress the background and detect almost all objects
and the saliency map is the closest to the ground truth map.
Conclusion
2
In this study
we propose a full-resolution salient object detection method that combines Boolean map and grayscale rarity. This method is robust to the size variation of salient objects and the diverse resolution of input images and can efficiently cope with the detection of small targets in complex backgrounds. Experimental results demonstrate that our method has the best comprehensive performance on our dataset and outperforms six traditional saliency models on the SED2 dataset. In addition
our method is computationally efficient on images with various sizes.
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