18994300
KDX: an indexer for support vector machines
Support vector machines (SVMs) have been adopted by many data mining and information-retrieval applications for learning a mining or query concept, and then retrieving the "top-k" best matches to the concept. However, when the data set is large, naively scanning the entire data set to find the top matches is not scalable. In this work, we propose a kernel indexing strategy to substantially prune the search space and, thus, improve the performance of top-k queries. Our kernel indexer (KDX) takes advantage of the underlying geometric properties and quickly converges on an approximate set of top-k instances of interest. More importantly, once the kernel (e.g., Gaussian kernel) has been selected and the indexer has been constructed, the indexer can work with different kernel-parameter settings (e.g., gamma and sigma) without performance compromise. Through theoretical analysis and empirical studies on a wide variety of data sets, we demonstrate KDX to be very effective. An earlier version of this paper appeared in the 2005 SIAM International Conference on Data Mining. This version differs from the previous submission in providing a detailed cost analysis under different scenarios, specifically designed to meet the varying needs of accuracy, speed, and space requirements, developing an approach for insertion and deletion of instances, presenting the specific computations as well as the geometric properties used in performing the same, and providing detailed algorithms for each of the operations necessary to create and use the index structure
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.
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.
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.
14264886
Geometric properties of watermarking schemes
A variety of image watermarking schemes have been proposed using orthogonal transformations, projections, and coding techniques to embed imperceptible watermarks into images. Analytical studies of the watermarking problem have typically been based on studying the performance limits of these known algorithms. In contrast, this paper formalizes the watermarking problem in an "algorithm independent" framework, representing any watermarking algorithm as a partition of the image space into a collection of sets, and defining requirements of these sets that must be met by any solution to the watermarking problem. Specifically, these requirements define and constrain the false-alarm ratio, distortion, robustness and security of a watermarking system. Using this formalism, we first characterize common features of algorithms that solve the watermarking problem. We show how the requirements defined earlier force important differences between watermarking signal sets and classical communication systems signal sets. Next, we show how common components of existing watermarking algorithms (e.g. transformations, projections, and coding) can be associated with specific requirements of the watermarking definition. Finally, we show how our new formalism of the watermarking problem provides a procedure for optimal design of watermarking systems to target specified false-alarm, distortion, and robustness objectives.
12117585
Distributed multivariate regression based on influential observations
Large-scale data sets are sometimes logically and physically distributed in separate databases. The issues of mining these data sets are not just their sizes, but also the distributed nature. The complication is that communicating all the data to a central database would be too slow. To reduce communication costs, one could compress the data during transmission. Another method is random sampling. We propose an approach for distributed multivariate regression based on sampling and discuss its relationship with the compression method. The central idea is motivated by the observation that, although communication is limited, each individual site can still scan and process all the data it holds. Thus it is possible for the site to communicate only influential samples without seeing data in other sites. We exploit this observation and derive a method that provides tradeoff between communication cost and accuracy. Experimental results show that it is better than the compression method and random sampling.
15998300
Shortest path amidst disc obstacles is computable
An open question in Exact Geometric Computation is whether there re 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 re collection of n discs.This problem can be solved in O (n 2 log n)time in the Real RAM model, but nothing was known about its computability in the standard (Turing) model of computation. We first show 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 single-exponential time upper bound when the input numbers are rational.Our result ppears to be the first example of non-algebraic combinatorial problem which is shown computable. It is also rare example of transcendental number theory yielding positive computational results.
6661888
A wavelet approach to foveating images
Motivated by applications of foveated images in visualization, we introduce the foveation transform of an image. We study the basic properties of these transforms using the multiresolution framework of Mallat. We also consider practical methods of realizing such transforms. In particular, we introduce a new method for foveating images based on wavelets. Preliminary experimental results are shown.