A robust approach for object matching and classification using Partial Dominant Orientation Descriptor
作者：
Highlights：
• We propose a novel descriptor for complex object matching and classification.
• The proposed descriptor combines both local and global properties of interest points.
• The proposed descriptor is highly distinctive and insensitive to common image transformations.
• Adaptive object distance is proposed to measure the similarity between objects.
• We compare our approach with many existing methods and we achieve the state-of-the-art performances on several benchmarks.
摘要
•We propose a novel descriptor for complex object matching and classification.•The proposed descriptor combines both local and global properties of interest points.•The proposed descriptor is highly distinctive and insensitive to common image transformations.•Adaptive object distance is proposed to measure the similarity between objects.•We compare our approach with many existing methods and we achieve the state-of-the-art performances on several benchmarks.
论文关键词：Object matching,Object classification,Image classification,Partial Dominant Orientation Descriptor,K-Nearest Neighbors,Adaptive object distance
论文评审过程：Received 8 April 2016, Revised 2 November 2016, Accepted 3 November 2016, Available online 14 November 2016, Version of Record 21 November 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.11.004
