46429314
Scalable Indexing for Perceptual Data
In recent years, multimedia objects such as images, video, and audio are becoming increasingly widespread. Many applications require content-based retrieval to be performed, and measurement of distance is a key component in such scenarios. The nature of multimedia requires perceptual similarity to be captured when computing distance between objects. Measures such as the Euclidean distance, which utilize all attributes of a pair of objects, do not perform very well. Instead, distance measures that use partial matches between objects have been found to perform significantly better. This is because, two multimedia objects can be considered perceptually similar when some respects closely match, even when they are very different in other respects. Existing distance measures that capture partial similarity have limitations, such as their non-metric nature, which makes scalable indexing challenging. In this paper, we propose the Partial Match Function, a distance measure that performs well for perceptual data, and allows efficient indexing.
18150673
The anatomy of a multimodal information filter
The proliferation of objectionable information on the Internet has reached a level of serious concern. To empower end-users with the choice of blocking undesirable and offensive websites, we propose a multimodal information filter, named MORF. In this paper, we present MORF's core components: its confidence-based classifier, a Cross-bagging ensemble scheme, and multimodal classification algorithm. Empirical studies and initial statistics collected from the MORF filters deployed at sites in the U.S. and Asia show that MORF is both efficient and effective, due to our classification methods.
9408458
PBIR-MM: multimodal image retrieval and annotation
We demonstrate PBIR-MM, an integrated system that we have built for conducting multimodal image retrieval. The system combines the strengths of content-based soft annotation (CBSA), multimodal relevance feedback through active learning, and perceptual distance formulation and indexing. PBIR-MM supports multimodal query and annotation in any combination of its three basic modes: seed-by-nothing, seed-by-keywords, and seed-by-content. We demonstrate PBIR-MM on a couple of very large image sets provided by image vendors and crawled from the Internet.
7236137
Efficient top-k hyperplane query processing for multimedia information retrieval
A query can be answered by a binary classifier, which separates the instances that are relevant to the query from the ones that are not. When kernel methods are employed to train such a classifier, the class boundary is represented as a hyperplane in a projected space. Data instances that are farthest from the hyperplane are deemed to be most relevant to the query, and that are nearest to the hyperplane to be most uncertain to the query. In this paper, we address the twin problems of efficient retrieval of the approximate set of instances (a) farthest from and (b) nearest to a query hyperplane. Retrieval of instances for this hyperplane-based query scenario is mapped to the range-query problem allowing for the reuse of existing index structures. Empirical evaluation on large image datasets confirms the effectiveness of our approach.
2439726
Concept boundary detection for speeding up SVMs
Support Vector Machines (SVMs) suffer from an O(n2) training cost, where n denotes the number of training instances. In this paper, we propose an algorithm to select boundary instances as training data to substantially reduce n. Our proposed algorithm is motivated by the result of (Burges, 1999) that, removing non-support vectors from the training set does not change SVM training results. Our algorithm eliminates instances that are likely to be non-support vectors. In the concept-independent preprocessing step of our algorithm, we prepare nearest-neighbor lists for training instances. In the concept-specific sampling step, we can then effectively select useful training data for each target concept. Empirical studies show our algorithm to be effective in reducing n, outperforming other competing downsampling algorithms without significantly compromising testing accuracy.
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.
9658544
Effect of Malicious Synchronization
We study the impact of malicious synchronization on computer systems that serve customers periodically. Systems supporting automatic periodic updates are common in web servers providing regular news update, sports scores or stock quotes. Our study focuses on the possibility of launching an effective low rate attack on the server to degrade performance measured in terms of longer processing time and request drops due to timeouts. The attackers are assumed to behave like normal users and send one request per update cycle. The only parameter utilized in the attack is the timing of the requests sent. By exploiting the periodic nature of the updates, a small number of attackers can herd users' update requests to a cluster and arrive in a short period of time. Herding can be used to discourage new users from joining the system and to modify the user arrival distribution, so that the subsequent burst attack will be effective. While the herding based attacks can be launched with a small amount of resource, they can be easily prevented by adding a small random component to the length of the update interval.
18025379
Error concealment for JPEG2000 images based on orthogonal edge directed filters
We propose an error concealment algorithm for JPEG2000 image transmissions over unreliable channels. Firstly, the local principal edge information in the damaged area is detected in the spatial domain. Then the proposed orthogonal edge directed filters (OEDFs) are applied to remove the ring or ripple artifact errors due to the loss of some wavelet transform (WT) bitplane data. Two kinds of constraints in WT domain are used for rectifying the recovered WT coefficients obtained from OEDFs, namely the WT known-value constraint and the empirical statistical constraint of the WT coefficients. Finally, this filtering-and-rectifying procedure is iterated until convergent. Simulation results have shown that both objective and subjective image quality have been improved by our proposed algorithm.
3144439
Fast rendering of foveated volumes in wavelet-based representation
A foveated volume can be viewed as a blending of multiple regions, each with a different level of resolution. It can be efficiently represented in the wavelet domain by retaining a small number of wavelet coefficients. We exploit the arrangement of those wavelet coefficients to achieve fast volume rendering. The running time is O(n2+m), where n is the width of the rendered image, and m is the number of wavelet coefficients retained for the foveated volume. Our algorithm consists of two phases. The first phase is a fast reconstruction of the super-voxels from the wavelet coefficients, and the second phase renders the super-voxels by carefully tracking rays with different thickness in the super-voxels. No expensive preprocessing on the wavelet coefficients is required. Hence, it is possible to interactively modify different viewing parameters like the transfer functions. A potential application of our algorithm is in remote visualization of large volume data-sets.
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.
9884959
Watermarking with retrieval systems
Abstract.We are interested in the problem of associating messages with multimedia content for the purpose of identifying them. This problem can be addressed by a watermarking system that embeds the associated messages into the multimedia content (also called Work). A drawback of watermarking is that the content will be distorted during embedding. On the other hand, if we assume that the database is available, the problem can be addressed by a retrieval system. Although no undesirable distortion is introduced when a retrieval system is used, the overhead of searching in large databases is fundamentally difficult (also known as the dimensionality curse). In this paper we present a novel framework that strikes a trade-off between watermarking and retrieval systems. Our framework avoids the dimensionality curse by introducing small distortions (watermark) into the multimedia content. From another perspective, the framework improves the watermarking performance, marked by significant reduction in distortion, by introducing searching ability in the message detection stage. To prove the concept, we give an algorithm based on the proposed notion of “active clustering”.