Image replica detection system utilizing R-trees and linear discriminant analysis

Abstract

This manuscript introduces a novel system for content-based identification of image replicas. The proposed approach utilizes image resemblance for deciding whether a test image has been replicated from a certain original or not. We formulate replica detection as a classification problem and show that we can optimize efficiency on a per query basis by dynamically solving a reduced multiclass problem. For this purpose, we investigate the effective coupling of multidimensional indexing and machine learning approaches and we aim to achieve replica detection through the training of classifiers with distortions expected in a replica. Visual descriptors are indexed using an R-tree based multidimensional structure for fast image retrieval. Cases unsuccessfully handled by the R-tree are resolved by a multiclass classifier operating on the transformed feature space that results from the application of linear discriminant analysis (LDA) and principal component analysis (PCA). Experimental results show that the proposed system can identify replicas with high accuracy and facilitate a wide range of applications such as copyright protection, content-based monitoring, content-aware multimedia management, etc.