11249256
A unified framework for resolving ambiguity in copy detection
Copy detection is an important component of digital rights management and can be implemented using a retrieval-based approach. Under this approach, a query image, suspected to be a copy, is compared against all the images in the owner database. The comparison is done based on a distance metric in feature space. The performance of such a system depends on the mutual separation of the feature representation of the images in the database. In this paper we propose a framework that increases this mutual separation by literally shifting them away from each other. The idea of modifying the features derives its inspiration from the field of watermarking. It is also important to make sure that the semantics of the images do not change after modification. Thus the focus of this paper is on how to modify the images in the database, so that the mutual separation between the images in feature space is above a certain threshold and the distortion induced is minimized. This problem can be formulated as a non-convex optimization problem which is difficult to solve. We propose a restriction of the problem and solve it using second-order cone programming. We present a practical implementation of our framework, named RAM, which uses AFMT as the feature representation. We conduct experiments to test the performance of RAM.
31284444
Error Resilient Image Authentication Using Feature Statistical and Spatial Properties
The pervasive distribution of digital images triggers an emergent need of authenticating degraded images by lossy compression and transmission. This paper proposes a robust content-based image authentication scheme for image transmissions over lossy channels. Content-based image authentication typically assesses authenticity based on a distance measure of feature differences between the testing image and its original. Commonly employed distance measures such as the Minkowski measures may not be adequate for content-based image authentication since they do not exploit statistical and spatial properties of the feature differences. This proposed error resilient scheme is based on a statistics- and spatiality-based measure (SSM) of feature differences. This measure is motivated by an observation that most malicious manipulations are localized whereas acceptable manipulations cause global distortions. Experimental results show that SSM is better than previous used measures in distinguishing malicious manipulations from acceptable ones, and the proposed SSM-based scheme is robust to transmission errors and other acceptable manipulations, and is sensitive malicious image modifications.
12953441
A Simultaneous Search Problem
Abstract. We introduce a new search problem motivated by computational metrology. The problem is as follows: we would like to locate two unknown numbers x,y ∈ [0,1] with as little uncertainty as possible, using some given number k of probes. Each probe is specified by a real number r∈ [0,1] . After a probe at r , we are told whether x≤ r or x \geq r , and whether y≤ r or y\geq r . We derive the optimal strategy and prove that the asymptotic behavior of the total uncertainty after k probes is 13/7 2-(k+1)/2 for odd k and 13/10 2-k/2 for even k .
42674644
Shortest Path amidst Disc Obstacles Is Computable
An open question in Exact Geometric Computation is whether there are transcendental computations that can be made "geometrically exact". Perhaps the simplest such problem in computational geometry is that of computing the shortest obstacle-avoiding path between two points p,q in the plane, where the obstacles are a collection of n discs. This problem can be solved in O(n2log n) time in the Real RAM model, but nothing was known about its computability in the standard (Turing) model of computation. We first give a direct proof of the Turing-computability of this problem, provided the radii of the discs are rationally related. We make the usual assumption that the numerical input data are real algebraic numbers. By appealing to effective bounds from transcendental number theory, we further show a single-exponential time upper bound when the input numbers are rational. Our result appears to be the first example of a non-algebraic combinatorial problem which is shown computable. It is also a rare example of transcendental number theory yielding positive computational results.
14013099
DynDex: a dynamic and non-metric space indexer
To date, almost all research work in the Content-Based Image Retrieval (CBIR) community has used Minkowski-like functions to measure similarity between images. In this paper, we first present a non-metric distance function, dynamic partial function (DPF), which works significantly better than Minkowski-like functions for measuring perceptual similarity; and we explain DPF's link to similarity theories in cognitive science. We then propose DynDex, an indexing method that deals with both the dynamic and non-metric aspects of the distance function. DynDex employs statistical methods including distance-based classification and bagging to enable efficient indexing with DPF. In addition to its efficiency for conducting similarity searches in very high-dimensional spaces, we show that DynDex remains effective when features are weighted dynamically for supporting personalized searches.
1679540
Hypersphere Indexer
Indexing high-dimensional data for efficient nearest-neighbor searches poses interesting research challenges. It is well known that when data dimension is high, the search time can exceed the time required for performing a linear scan on the entire dataset. To alleviate this dimensionality curse, indexing schemes such as locality sensitive hashing (LSH) and M-trees were proposed to perform approximate searches. In this paper, we propose a hypersphere indexer, named Hydex, to perform such searches. Hydex partitions the data space using concentric hyperspheres. By exploiting geometric properties, Hydex can perform effective pruning. Our empirical study shows that Hydex enjoys three advantages over competing schemes for achieving the same level of search accuracy. First, Hydex requires fewer seek operations. Second, Hydex can maintain sequential disk accesses most of the time. And third, it requires fewer distance computations.
10743717
Support vector machine active learning for image retrieval
Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user's desired output or query concept by asking the user whether certain proposed images are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval. The algorithm selects the most informative images to query a user and quickly learns a boundary that separates the images that satisfy the user's query concept from the rest of the dataset. Experimental results show that our algorithm achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
14716350
Why one example is not enough for an image query
For over a decade, query-by-one-example has been a popular query paradigm for multimedia information retrieval. We show, by analyzing feature-to-semantics mapping, that such a paradigm cannot realistically lead to a scalable, satisfactory query performance. More specifically, we cluster a small image dataset based on the images' perceptual features, and show that these image clusters are not coherent to the semantic categories of the images. Though some image categories are well separated from the others in the input space formed by the perceptual features, most categories are colocated in more than one cluster. For a query-concept that is mixed with others in a number of clusters, the query-by-one-example paradigm simply lacks information to identify clearly the target query-concept, and hence cannot achieve satisfactory query results.
10654167
Enhanced perceptual distance functions and indexing for image replica recognition
The proliferation of digital images and the widespread distribution of digital data that has been made possible by the Internet has increased problems associated with copyright infringement on digital images. Watermarking schemes have been proposed to safeguard copyrighted images, but watermarks are vulnerable to image processing and geometric distortions and may not be very effective. Thus, the content-based detection of pirated images has become an important application. In this paper, we discuss two important aspects of such a replica detection system: distance functions for similarity measurement and scalability. We extend our previous work on perceptual distance functions, which proposed the Dynamic Partial Function (DPF), and present enhanced techniques that overcome the limitations of DPF. These techniques include the Thresholding, Sampling, and Weighting schemes. Experimental evaluations show superior performance compared to DPF and other distance functions. We then address the issue of using these perceptual distance functions to efficiently detect replicas in large image data sets. The problem of indexing is made challenging by the high-dimensionality and the nonmetric nature of the distance functions. We propose using Locality Sensitive Hashing (LSH) to index images while using the above perceptual distance functions and demonstrate good performance through empirical studies on a very large database of diverse images.