Polygon-shape-based Scale and Rotation Invariant Features for camera-based document image retrieval

The scientific problem of real-time camera-based document image retrieval is achieved by computing the image features adapted to this acquisition mode i.e. the image features which are highly discriminative even under challenging conditions of camera capture as well as which are light to be computed. In this paper, we propose new extension features to our previously proposed SRIF descriptor. The new descriptor is named as PSRIF (Polygon-shape-based Scale and Rotation Invariant Features) and makes SRIF more discriminative under challenging camera capture conditions by using least number of nearest points around the keypoint. We propose to use angles and edges of the polygon established from nearest points as additional features. To validate our extension features (PSRIF), the experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 × 11768 pixels resolution) and 700 textual document images. The experimental results show that our extension features (PSRIF) improve the performance of SRIF as well as PSRIF outperforms the state-of-the-art methods.

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