Diagram Image Retrieval Using Sketch-Based Deep Learning and Transfer Learning

CVPR 2020

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Authors: Manish Bhattarai, Diane Oyen, Juan Castorena, Liping Yang, Brendt Wohlberg Description: Resolution of the complex problem of image retrieval for diagram images has yet to be reached. Deep learning methods continue to excel in the fields of object detection and image classification applied to natural imagery. However, the application of such methodologies applied to binary imagery remains limited due to lack of crucial features such as textures,color and intensity information. This paper presents a deep learning based method for image-based search for binary patent images by taking advantage of existing large natural image repositories for image search and sketch-based methods. (Sketches are not identical to diagrams, but they do share some characteristics; for example, they both are gray scale (binary), composed of contours, and lacking in texture are key features for both imagery types.) We begin by using deep learning to generate sketches from natural images for image retrieval and then train a second deep learning model on the sketches. We then use our small set of manually labeled patent diagram images via transfer learning to adapt the image search from sketches of natural images to diagrams. Our experiment results show the effectiveness of deep learning with transfer learning for detecting near-identical copies in patent images and querying similar images based on content.

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