Objective

2

Saliency detection

as a preprocessing component of computer vision

has received increasing attention in the areas of object relocation

scene classification

semantic segmentation

and visual tracking. Although object detection has been greatly developed

it remains challenging because of a series of realistic factors

such as background complexity and attention mechanism. In the past

many significant target detection methods have been developed. These methods are mainly divided into traditional methods and new methods based on deep learning. The traditional approach is to find significant targets through low-level manual features

such as contrast

color

and texture. These general techniques are proven effective in maintaining image structure and reducing computational effort. However

these low-level features cause difficulty in capturing high-level semantic knowledge about objects and their surroundings. Therefore

these low-level feature-based methods do not achieve excellent results when salient objects are stripped from the stacked background. The saliency detection method based on deep learning mainly seeks significant targets by automatically extracting advanced features. However

most of these advanced models focus on the nonlinear combination of advanced features extracted from the final convolutional layer. The boundaries of salient objects are often extremely blurry due to the lack of low-level visual information such as edges. In these jobs

convolutional neural network (CNN) features are applied directly to the model without any processing. The features extracted from the CNN are generally high in dimension and contain a large amount of noise

thereby reducing the utilization efficiency of CNN features and revealing an opposite effect. Sparse methods can effectively aggregate the salient objects in a feature map and eliminate some of the noise interference. Sparse self-encoding is a sparse method. A traditional saliency recognition method based on sparse self-encoding and image fusion

combined with background prior and contrast analysis and VGG (visual geometry group) saliency calculation

is proposed to solve these problems.

Method

2

The proposed algorithm is mainly composed of the following:traditional saliency map extraction

VGG feature extraction

sparse self-encoding

and saliency result optimization. The traditional method to be improved is selected

and the corresponding saliency map is calculated. In this experiment

we select four traditional methods with excellent results

namely

discriminative regional feature integration (DRFI)

high-dimensional color transform (HDCT)

regularized random walks ranking (RRWR)

and contour-guided visual search (CGVS). Then

the VGG network is used to extract feature maps. The feature maps obtained by each pooled layer are sparsely self-encoded to obtain 25 sparse saliency feature maps. When a feature map is selected

excessive edge information and texture information are retained because the features extracted by the first three pooling layers are mainly low-level features

indicating duplicate effects with feature maps obtained by the conventional method; thus

the feature maps from low-level are not used. The comparison between the fourth and fifth feature maps shows that the feature information of the fifth pooling layer is excessively lost. After experimental verification

the fifth layer characteristic map exerts an interference effect. Thus

we use the feature map extracted from the fourth pooling layer. Then

these feature maps are placed into the sparse self-encoder to perform the sparse operation to obtain five feature maps. Each feature map is integrated with the corresponding saliency map obtained in the previous volume. Finally

the neural network performs the operation and calculates the final saliency map.

Result

2

Our experiments involved four open datasets:DUT-OMRON

ECSSD

HKU-IS

and MSRA. Then

we obtained half of the images from the four datasets used in the experiment to form a training set and the remaining four test sets. The results obtained can be extremely credible. The following conclusions are drawn from the experiment. 1) The proposed model greatly improves the F value in the four datasets of the four methods

including an increase of 24.53% in the HKU-IS dataset of the DRFI method. 2) The MAE (mean absolute error) value has also been greatly reduced

the least of which is reduced by 12.78% for the ECSSD dataset of the CGVS method and the highest of which is reduced by nearly 50%. 3) The proposed model network has few layers

few parameters

and short calculation time. The training time is approximately 2 h

and the average test time of the image is approximately 0.2 s. On the contrary

Liu chooses an image saliency optimization scheme using adaptive fusion. The training time is approximately 47 h

and the average test time of the image is 56.95 s. The proposed model greatly improves the computational efficiency. 4) The proposed model achieves a significant improvement for the four datasets

especially the HKU-IS and MSRA datasets. These datasets contain difficult images

thereby confirming the effectiveness of the proposed method.

Conclusion

2

A low-level feature map based on traditional models

such as a texture and high-level feature map of a sparsely self-encoded VGG network

is proposed to optimize saliency results and greatly improve saliency target recognition. The traditional methods based on DRFI

HDCT

RRWR

and CGVS are tested in the publicly significant object detection datasets DUT-OMRON

ECSSD

HKU-IS

and MSRA

respectively. The obtained F value and MAE value are significantly improved

thereby confirming the effectiveness of the proposed method. Moreover

the method steps and network structure are simple and easy to understand

the training takes little time

and popular promotion can be easily obtained. The limitation of the study is that some of the extracted feature maps are missing. In practice

only the fourth layer of VGG maps is selected

and not all useful information is fully utilized.