11608556
MORF: A Distributed Multimodal Information Filtering System
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 Web-sites, we propose a multimodal personalized information filter, named MORF. The design of MORF aims to meet three major performance goals: efficiency, accuracy, and personalization. To achieve these design goals, we have devised a multimodality classification algorithm and a personalization algorithm. Empirical study 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, compared to the traditional URL- and text-based filtering approaches.
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.
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.
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.
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.
3079245
Statistics- and Spatiality-Based Feature Distance Measure for Error Resilient Image Authentication
Content-based image authentication typically assesses authenticity based on a distance measure between the image to be tested and its original. Commonly employed distance measures such as the Minkowski measures (including Hamming and Euclidean distances) may not be adequate for content-based image authentication since they do not exploit statistical and spatial properties in features. This paper proposes a feature distance measure for content-based image authentication based on statistical and spatial properties of the feature differences. The proposed statistics- and spatiality-based measure (SSM) is motivated by an observation that most malicious manipulations are localized whereas acceptable manipulations result in global distortions. A statistical measure, kurtosis, is used to assess the shape of the feature difference distribution; a spatial measure, the maximum connected component size, is used to assess the degree of object concentration of the feature differences. The experimental results have confirmed that our proposed measure is better than previous measures in distinguishing malicious manipulations from acceptable ones.
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.
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.
472683
On the effectiveness of DDoS attacks on statistical filtering
Distributed denial of service (DDoS) attacks pose a serious threat to service availability of the victim network by severely degrading its performance. Recently, there has been significant interest in the use of statistical-based filtering to defend against and mitigate the effect of DDoS attacks. Under this approach, packet statistics are monitored to classify normal and abnormal behaviour. Under attack, packets that are classified as abnormal are dropped by the filter that guards the victim network. We study the effectiveness of DDoS attacks on such statistical-based filtering in a general context where the attackers are "smart". We first give an optimal policy for the filter when the statistical behaviours of both the attackers and the filter are static. We next consider cases where both the attacker and the filter can dynamically change their behaviour, possibly depending on the perceived behaviour of the other party. We observe that while an adaptive filter can effectively defend against a static attacker, the filter can perform much worse if the attacker is more dynamic than perceived.