Re-ranking for Improved Image Query-Based Search

Abstract

In image search, an algorithm tries to identify images in a database that are similar to a query image. Image search has numerous applications. For example, image search can help historians find images of a historical building from a large image database of buildings worldwide. Feature extraction and nearest neighbors methods are standard steps in the image search task. Current state-of-the-art image search engines extract features from images through CNNs. Next, they find top-n images closest to the query image by nearest neighbor search (NN search). After NN search, the engine outputs results. However, it is still hard for current image search technologies to identify images under complicated situations, such as illumination, viewpoints, and rotation changes. These changes will reduce the accuracy of the engine. Therefore, how to improve the accuracy of the image search engine is still a challenging task.

Researchers want to design re-ranking methods for image search to improve accuracy. According to previous research, there are mainly two types of re-ranking: global feature-based re-ranking and local feature-based re-ranking. Furthermore, both types of re-ranking methods could improve the accuracy of the image search engine. However, previous re-ranking methods are not fast or accurate enough. We implement novel global and local feature-based re-ranking methods to improve the accuracy of image search significantly. We test our re-ranking methods on popular image search databases. Experiments show that our re-ranking methods improve accuracy while slightly increasing computation time.