Object-aware deep feature extraction for feature matching |
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| Authors: | Zuoyong Li Weice Wang Taotao Lai Haiping Xu Pantea Keikhosrokiani |
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| Affiliation: | 1. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China;2. Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing, Yango University, Fuzhou, China;3. College of Mathematics and Data Science, Minjiang University, Fuzhou, China;4. School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia Faculty of Information Technology and Electrical Engineering, Faculty of Medicine, University of Oulu, Oulu, Finland |
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| Abstract: | Feature extraction is a fundamental step in the feature matching task. A lot of studies are devoted to feature extraction. Recent researches propose to extract features by pre-trained neural networks, and the output is used for feature matching. However, the quality and the quantity of the features extracted by these methods are difficult to meet the requirements for the practical applications. In this article, we propose a two-stage object-aware-based feature matching method. Specifically, the proposed object-aware block predicts a weighted feature map through a mask predictor and a prefeature extractor, so that the subsequent feature extractor pays more attention to the key regions by using the weighted feature map. In addition, we introduce a state-of-the-art model estimation algorithm to align image pair as the input of the object-aware block. Furthermore, our method also employs an advanced outlier removal algorithm to further improve matching quality. Experimental results show that our object-aware-based feature matching method improves the performance of feature matching compared with several state-of-the-art methods. |
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| Keywords: | feature matching image alignment model estimation object-aware outlier removal |
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