VisHash: visual similarity preserving image hashing for diagram retrieval

Abstract

We present VisHash to address the problem of retrieving copies of images, particularly drawings and diagrams used in technical documents. While these images convey important technical information, it is difficult to search for these images. Recent advances in computer vision using deep learning methods have significantly advanced our ability to analyze and retrieve natural images, yet most of these advances do not directly apply to drawings and diagrams due to the very different nature of low-level features of the images. We find that classic computer vision techniques on patch-based extraction of image features such as relative brightness work better on drawings and diagrams compared to frequency-based similarity-preserving perceptual hashes; yet existing relative-brightness signatures often fail to calculate any meaningful signature due to the sparsity of information in technical drawings. We take advantage of the effectiveness of the relative-brightness signature and extend the approach to develop VisHash, a visual similarity preserving signature that works well on technical diagrams. Importantly, we demonstrate the high level of precision of VisHash for image retrieval compared with competing image hashes of large sets of real drawings from patents and technical images from the web. VisHash is available as open-source code to incorporate into image search and indexing workflows.