Abstract：Image similarity measurement is crucial to many vision applications. A similarity measurement method based on adaptive weighting of similarity matrix is proposed in this paper. The image is firstly divided into the same-sized patches, and the convolutional neural networks are adopted to construct the descriptor of each patch. The patch similarities are calculated to constitute the similarity matrix. The probability of image pair coming from the same place is evaluated by analyzing the data distribution in similarity matrix. And the similarity weight of each unit is calculated based on the data difference. Ultimately, the overall image similarity is determined. The experimental results show that the proposed method is more robust than the existing ones in image retrieval. Moreover, it effectively solves the loop closure detection in simultaneous localization and mapping.
李钦，游雄，李科，汤奋. 基于相似矩阵自适应加权的实景图像相似度计算方法*[J]. 模式识别与人工智能, 2017, 30(11): 1003-1011.
LI Qin, YOU Xiong, LI Ke, TANG Fen. Geography Image Similarity Measurement Method Based on Adaptive Weighting of Similarity Matrix. , 2017, 30(11): 1003-1011.
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