Saliency Detection Based on Multiple-Level Feature Learning

Author:

Li Xiaoli123456,Liu Yunpeng12345,Zhao Huaici12345ORCID

Affiliation:

1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China

2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China

3. University of Chinese Academy of Sciences, Beijing 100049, China

4. Key Laboratory of Opto-Electronic Information Processing, Shenyang 110169, China

5. The Key Lab of Image Understanding and Computer Vision, Shenyang 110619, China

6. School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang 110168, China

Abstract

Finding the most interesting areas of an image is the aim of saliency detection. Conventional methods based on low-level features rely on biological cues like texture and color. These methods, however, have trouble with processing complicated or low-contrast images. In this paper, we introduce a deep neural network-based saliency detection method. First, using semantic segmentation, we construct a pixel-level model that gives each pixel a saliency value depending on its semantic category. Next, we create a region feature model by combining both hand-crafted and deep features, which extracts and fuses the local and global information of each superpixel region. Third, we combine the results from the previous two steps, along with the over-segmented superpixel images and the original images, to construct a multi-level feature model. We feed the model into a deep convolutional network, which generates the final saliency map by learning to integrate the macro and micro information based on the pixels and superpixels. We assess our method on five benchmark datasets and contrast it against 14 state-of-the-art saliency detection algorithms. According to the experimental results, our method performs better than the other methods in terms of F-measure, precision, recall, and runtime. Additionally, we analyze the limitations of our method and propose potential future developments.

Funder

Science and Technology Innovation Key Fund project of Chinese Academy of Sciences

Publisher

MDPI AG

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