Srif: Scale And Rotation Invariant Features For Camera-Based Document Image Retrieval
2015 13th International Conference on Document Analysis and Recognition (ICDAR)(2015)
摘要
In this paper, we propose a new feature vector, named Scale and Rotation Invariant Features (SRIF), for real-time camera-based document image retrieval. SRIF is based on Locally Likely Arrangement Hashing (LLAH), which has been widely used and accepted as an efficient real-time camera-based document image retrieval method based on text. SRIF is computed based on geometrical constraints between pairs of nearest points around a keypoint. It can deal with feature point extraction errors which are introduced as a result of the camera capturing of documents. The experimental results show that SRIF outperforms LLAH in terms of retrieval accuracy and processing time.
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关键词
SRIF,scale and rotation invariant features,camera-based document image retrieval,feature vector,locally likely arrangement hashing,LLAH,real-time camera-based document image retrieval method,geometrical constraints,camera
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