Book Page Identification Using Convolutional Neural Networks Trained by Task-Unrelated Dataset

摘要

This paper presents a pipeline to make convolutional neural networks (CNNs) trained for another unrelated task available for book page identification. The pipeline has five building blocks: (1) An image segmentation module to separate book page from the background; (2) An image correction module to correct geometry and color distortions; (3) A feature extraction module to extract discriminative image features by a pre-trained CNN; (4) A feature compression module to reduce feature dimensions for speeding up; and (5) A feature matching module to calculate the similarity between a query image and a reference image, and then to find out the most similar reference image. The experimental results on a challenging testing dataset show that the proposed book page identification method achieves a top-5 hit rate of 98.93%.