Object-aware deep feature extraction for feature matching

Abstract

Summary

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.