Image similarity search in large databases using a fast machine learning approach

Abstract

Today's tendency to protect various copyrighted multimedia contents, such as text, images or video, resulted in many algorithms for detecting duplicates. If the observed content is identical, then the task is easy. But if the content is even slightly changed, the task to identify the duplicate can be difficult and time consuming. In this paper we develop a fast, two-step algorithm for detecting image duplicates. The algorithm finds also slightly changed images with added noise, translated or scaled content, or images having been compressed and decompressed by various algorithms. The time needed to detect duplicates is kept low by implementing image feature-based searches. To detect all similar images for a given reference image, the feature extraction based on convex layers is deployed. The correlation coefficient between two features gives the first hint of similarity to the user, who creates a learning set for support vector machines by simple on-screen selection. © 2008 Springer-Verlag Berlin Heidelberg.