Indexing structure plays an important role in the application of fast near-duplicate image detection, since it can narrow down the search space. In this article, we develop a cluster of uniform randomized trees (URTs) as an efficient indexing structure to perform fast near-duplicate image detection. The main contribution in this article is that we introduce “uniformity” and “randomness” into the indexing construction. The uniformity requires classifying the object images into the same scale subsets. Such a decision makes good use of the two facts in near-duplicate image detection, namely: (1) the number of categories is huge; (2) a single category usually contains only a small number of images. Therefore, the uniform distribution is very beneficial to narrow down the search space and does not significantly degrade the detection accuracy. The randomness is embedded into the generation of feature subspace and projection direction, improveing the flexibility of indexing construction. The experimental results show that the proposed method is more efficient than the popular locality-sensitive hashing and more stable and flexible than the traditional KD-tree.

Video sequence matching aims to locate a query video clip in a video database. It plays an important role in reducing storage redundancy and detecting video copies for copyright protection. In this paper, we propose an effective method for video sequence matching based on the invariance of color correlation. The proposed method first splits each key-frame into nonoverlapping blocks. For each block, we sort the red, green, and blue color components according to their average intensities, and use the percentage of the color correlation to generate a frame feature with a small size. Finally, the resulting video feature is made up of the consecutive frame features, which is demonstrated to be robust against most typical video content-preserving operations, including geometric distortion, blurring, noise contamination, contrast enhancement, and strong re-encoding. The experimental results show that the proposed method outperforms the existing methods in the literature, as well as the method based on the traditional color histogram. Furthermore, the time and space complexity of our algorithm are both satisfactory, which are very important for many real-time applications.

This paper presents a framework for near-duplicate image detection in a visually salient Riemannian space. A visual saliency model is first used to identify salient regions of the image and then the salient region covariance matrix (SCOV) of various image features is computed. SCOV, which lies in a Riemannian manifold, is used as a robust and compact image content descriptor. An efficient coarse-to-fine Riemannian (CTOFR) image search strategy has been developed to improve efficiency while maintaining accuracy. CTOFR first uses a computationally fast but less accurate log-Euclidean Riemannian metric to do a coarse level search of the entire database and retrieve a subset of likely targets and then uses a computationally expensive but more accurate affine-invariant Riemannian metric to search the returns from the coarse search. We present experimental results to demonstrate that SCOV is a very compact, robust, and discriminative descriptor which is competitive to other state-of-the-art descriptors for near-duplicate image and video detection. We show that CTOFR can yield significant speedups over traditional full search methods without sacrificing accuracy, and that the larger the database the higher the speedup factor.

Copy detection aims to identify various content-preserving copies from the same origin and distinguish the different sources. In this paper, we propose a novel feature for image copy detection based on the radon transform (RT). We first theoretically analyze that the proposed feature is invariant against rotation, scaling and translation (RST) operations and robust to lossy JPEG compression and additive noises. The feature can meet the robustness requirements of copy detection applications. The proposed feature is statistically dependent of image content and is capable of capturing unique information of the image for fragility of the copy detection system. The experimental results show that the proposed scheme outperforms the existing methods, when detecting image copies subjected to various transformations, in terms of receiver operating characteristic (ROC) curves.