9936649
A differential LSI method for document classification
We have developed an effective probabilistic classifier for document classification by introducing the concept of the differential document vectors and DLSI (differential latent semantics index) spaces. A simple posteriori calculation using the intraand extra-document statistics demonstrates the advantage of the DLSI space-based probabilistic classifier over the popularly used LSI space-based classifier in classification performance.
3257950
Robustness of Regional Matching Scheme over Global Matching Scheme
Analyzing the effect of concentrated noise on a typical decision-making process of a simplified two-candidate voting model, we have demonstrated that a local approach using a regional matching process is more robust and stable than a direct approach using a global matching process, by establishing that the former is capable of accommodating a higher level of noise than the latter before the result of the decision overturns. To extend the theory to imagery analysis, we pose a conjecture that our conclusion on the robustness of the regional matching processes remains valid not only for the simpler vote counting schemes but also for practically more important decision-making schemes in image analysis which involve dimension-reducing transforms or other features extraction processes such as principal component analysis or Gablor transforms. Two convincing experimental verifications are provided, supporting not only the theory by a white-black flag recognition problem on a pixel-by-pixel basis, but also the validity of the conjecture by a facial recognition problem in the presence of localized noise typically represented by clutter or occlusion in imagery.
5771208
Image annotation by large-scale content-based image retrieval
Image annotation has been an active research topic in recent years due to its potentially large impact on both image understanding and Web image search. In this paper, we target at solving the automatic image annotation problem in a novel search and mining framework. Given an uncaptioned image, first in the search stage, we perform content-based image retrieval (CBIR) facilitated by high-dimensional indexing to find a set of visually similar images from a large-scale image database. The database consists of images crawled from the World Wide Web with rich annotations, e.g. titles and surrounding text. Then in the mining stage, a search result clustering technique is utilized to find most representative keywords from the annotations of the retrieved image subset. These keywords, after salience ranking, are finally used to annotate the uncaptioned image. Based on search technologies, this framework does not impose an explicit training stage, but efficiently leverages large-scale and well-annotated images, and is potentially capable of dealing with unlimited vocabulary. Based on 2.4 million real Web images, comprehensive evaluation of image annotation on Corel and U. Washington image databases show the effectiveness and efficiency of the proposed approach.
2454682
Mining Association Rules from Structural Deltas of Historical XML Documents
Previous work on XML association rule mining focuses on mining from the data existing in XML documents at a certain time point. However, due to the dynamic nature of online information, an XML document typically evolves over time. Knowledge obtained from mining the evolvement of an XML document would be useful in a wide range of applications, such as XML indexing, XML clustering. In this paper, we propose to mine a novel type of association rules from a sequence of changes to XML structure, which we call XML Structural Delta Association Rule (XSD-AR). We formulate the problem of XSD-AR mining by considering both the frequency and the degree of changes to XML structure. An algorithm, which is derived from the FP-growth, and its optimizing strategy are developed for the problem. Preliminary experiment results show that our algorithm is efficient and scalable at discovering a complete set of XSD-ARs.
13928372
VRules: an effective association-based classifier for videos
Video classification is an important step towards multimedia understanding. Most state-of-the-art approaches which apply HMM to capture the temporal information of videos have the limitation by assuming that the current state of a video depends only on the immediate previous state. Nevertheless, this assumption may not hold for videos of various categories. In this paper, we present an effective video classifier which employs the association rule mining technique to discover the actual dependence relationship between video states. The discriminatory state transition patterns mined from different video categories are then used to perform classification. Besides capturing the association between states in the time space, we also capture the association between low-level features in spatial dimension to further distinguish the semantics of videos. Experimental results show that the performance of our association rule based classifier is quite promising.
14133000
Software-based diagnosis for processors
Software-based self-test (SBST) is emerging as a promising technology for enabling at-speed test of high-speed microprocessors using low-cost testers. We explore the fault diagnosis capability of SBST, in which functional information can be used to guide and facilitate the generation of diagnostic tests. By using a large number of carefully constructed diagnostic test programs, the fault universe can be divided into fine-grained partitions, each corresponding to a unique pass/fail pattern. We evaluate the quality of diagnosis by constructing diagnostic-tree-based fault dictionaries. We demonstrate the feasibility of the proposed method by applying it to a processor example. Experimental results show its potential as an effective method for diagnosing larger processors.
206929170
Optimizing the Fast Fourier Transform on a Multi-core Architecture
The rapid revolution in microprocessor chip architecture due to multicore technology is presenting unprecedented challenges to the application developers as well as system software designers: how to best exploit the parallelism potential due to such multi-core architectures? In this paper, we report an in-depth study on such challenges based on our experience of optimizing the fast Fourier transform (FFT) on the IBM Cyclops-64 chip architecture - a large-scale multi-core chip architecture consisting 160 thread units, associated memory banks and an interconnection network that connect them together in a shared memory organization. We demonstrate how multi-core architectures like the C64 could be used to achieve a high performance implementation of FFT both in 1D and 2D cases. We analyze the optimization challenges and opportunities including problem decomposition, load balancing, work distribution, and data-reuse, together with the exploiting of the C64 architecture features such as the multi-level of memory hierarchy and large register files. Furthermore, the experience learned during the hand-tuned optimization process have provided valuable guidance in our compiler optimization design and implementation. The main contributions of this paper include: 1) our study demonstrates that successful optimization for C64-like large-scale multi-core architectures requires a careful analysis that can identify certain domain-specific features of a target application (e.g. FFT) and match them well with some key multi-core architecture features; 2) our optimization, assisted with hand-tuned process, provided quantitative evidence on the importance of each optimization identified in 1); 3) automatic optimization by our compiler, the design and implementation of which is guided by the feedbacks from 1) and 2), shows excellent results that are often comparable to the results derived from our time-consuming hand-tuned code.