17411055
Removal of compression artifacts using projections onto convex sets and line process modeling
We present a new image recovery algorithm to remove, in addition to blocking, ringing artifacts from compressed images and video. This new algorithm is based on the theory of projections onto convex sets (POCS). A new family of directional smoothness constraint sets is defined based on line processes modeling of the image edge structure. The definition of these smoothness sets also takes into account the fact that the visibility of compression artifacts in an image is spatially varying. To overcome the numerical difficulty in computing the projections onto these sets, a divide-and-conquer (DAC) strategy is introduced. According to this strategy, new smoothness sets are derived such that their projections are easier to compute. The effectiveness of the proposed algorithm is demonstrated through numerical experiments using Motion Picture Expert Group based (MPEG-based) coders-decoders (codecs).
2474873
A support vector machine approach for detection of microcalcifications
We investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to outperform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.
15157984
Image morphing based on mutual information and optimal mass transport
Time domain image interpolation, or image morphing, refers to a class of techniques for generating a series of smoothly changing intermediate images between two given related images. In this note, we present a novel approach based on the theoiy of optimal mass transport, using mutual information (MI) as the similarity measurement. The potential applications also include image registmtion, compression and coding.
6243197
An application of least squares fit mapping to text information retrieval
This paper describes a unique example-based mapping method for document retrieval. We discovered that the knowledge about relevance among queries and documents can be used to obtain empirical connections between query terms and the canonical concepts which are used for indexing the content of documents. These connections do not depend on whether there are shared terms among the queries and documents; therefore, they are especially effective for a mapping from queries to the documents where the concepts are relevant but the terms used by article authors happen to be different from the terms of database users. We employ a Linear Least Squares Fit (LLSF) technique to compute such connections from a collection of queries and documents where the relevance is assigned by humans, and then use these connections in the retrieval of documents where the relevance is unknown. We tested this method on both retrieval and indexing with a set of MEDLINE documents which has been used by other information retrieval systems for evaluations. The effectiveness of the LLSF mapping and the significant improvement over alternative approaches was evident in the tests.
18309490
Structure Pruning Strategies for Min-Max Modular Network
The min-max modular network has been shown to be an efficient classifier, especially in solving large-scale and complex pattern classification problems. Despite its high modularity and parallelism, it suffers from quadratic complexity in space when a multiple-class problem is decomposed into a number of linearly separable problems. This paper proposes two new pruning methods and an integrated process to reduce the redundancy of the network and optimize the network structure. We show that our methods can prune a lot of redundant modules in comparison with the original structure while maintaining the generalization accuracy.
6002635
Improving text categorization methods for event tracking
Automated tracking of events from chronologically ordered document streams is a new challenge for statistical text classification. Existing learning techniques must be adapted or improved in order to effectively handle difficult situations where the number of positive training instances per event is extremely small, the majority of training documents are unlabelled, and most of the events have a short duration in time. We adapted several supervised text categorization methods, specifically several new variants of the k-Nearest Neighbor (kNN) algorithm and a Rocchio approach, to track events. All of these methods showed significant improvement (up to 71% reduction in weighted error rates) over the performance of the original kNN algorithm on TDT benchmark collections, making kNN among the top-performing systems in the recent TDT3 official evaluation. Furthermore, by combining these methods, we significantly reduced the variance in performance of our event tracking system over different data collections, suggesting a robust solution for parameter optimization.
8719734
Association rules over interval data
We consider the problem of mining association rules over interval data (that is, ordered data for which the separation between data points has meaning). We show that the measures of what rules are most important (also called rule interest) that are used for mining nominal and ordinal data do not capture the semantics of interval data. In the presence of interval data, support and confidence are no longer intuitive measures of the interest of a rule. We propose a new definition of interest for association rules that takes into account the semantics of interval data. We developed an algorithm for mining association rules under the new definition and overview our experience using the algorithm on large real-life datasets.