Younghee Kwon

Younghee Kwon

Authored Publications
Google Publications
Other Publications
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    Tenzing A SQL Implementation On The MapReduce Framework
    Biswapesh Chattopadhyay
    Liang Lin
    Weiran Liu
    Sagar Mittal
    Prathyusha Aragonda
    Vera Lychagina
    Michael Wong
    Proceedings of VLDB, VLDB Endowment(2011), pp. 1318-1327
    Preview abstract Tenzing is a query engine built on top of MapReduce for ad hoc analysis of Google data. Tenzing supports a mostly complete SQL implementation (with several extensions) combined with several key characteristics such as heterogeneity, high performance, scalability, reliability, metadata awareness, low latency, support for columnar storage and structured data, and easy extensibility. Tenzing is currently used internally at Google by 1000+ employees and serves 10000+ queries per day over 1.5 petabytes of compressed data. In this paper, we describe the architecture and implementation of Tenzing, and present benchmarks of typical analytical queries. View details
    Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
    Kwang In Kim
    IEEE Transactions on Pattern Analysis & Machine Intelligence, 32(2010), pp. 1127-1133
    Preview abstract This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based on example pairs of input and output images. Kernel ridge regression (KRR) is adopted for this purpose. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing algorithms shows the effectiveness of the proposed method. View details
    Example-Based Learning for Single-Image Super-Resolution
    Kwang In Kim
    Proceedings of Pattern Recognition, 30th DAGM Symposium, Munich, Germany, June 10-13, 2008, pp. 456-465
    Preview abstract This paper proposes a regression-based method for single-image super-resolution. Kernel ridge regression (KRR) is used to estimate the high-frequency details of the underlying high-resolution image. A sparse solution of KRR is found by combining the ideas of kernel matching pursuit and gradient descent, which allows time-complexity to be kept to a moderate level. To resolve the problem of ringing artifacts occurring due to the regularization effect, the regression results are post-processed using a prior model of a generic image class. Experimental results demonstrate the effectiveness of the proposed method. View details
    Anisotropic Total Variation Method for Text Image Super-Resolution
    Battulga Bayarsaikhan
    Jin Hyung Kim
    Proceedings of the Eighth International Workshop on Document Analysis Systems (DAS), Nara, Japan, September 16-19, 2008, pp. 473-479
    Preview abstract This paper presents a text image super resolution algorithm based on total variation (TV). Text images typically consist of slim strokes on background. Thus, there are three different local characteristics as homogeneous, directed and complex on text image. Homogeneous region corresponds to background and directed means the region with dominant stroke direction and remaining is complex region. We proposed higher order smoothing on homogeneous region and anisotropic regularization on directed region which encodes the preference of edge direction by smoothing along preferred direction only. Required regularization terms are combined in proposed anisotropic TV functional and controlled by relating parameters. We calculated relating parameters byutilizing structure tensor field. Also to reduce the computational cost, we previously estimated nonchanging pixels and exclude them from calculation for speed up. Experiments shown that, proposed method performs better with low computational cost than general purpose TV on text image. View details
    Stroke Verification with Gray-level Image for Hangul Video Text Recognition
    Jinsik Kim
    Seonghun Lee
    Jin Hyung Kim
    Proceedings of 18th International Conference on Pattern Recognition (ICPR), Hong Kong, China, August 20-24, 2006, pp. 1074-1077
    Preview abstract Traditional OCR uses binarization technique, which makes OCR simple. But it makes strokes ambiguous and that causes recognition errors. Main reason of those errors is similar grapheme pair confusing error. It can be reduced by verifying ambiguous area of gray level image. After checking whether there is similar grapheme pair by analyzing traditional OCR result candidates, the base stroke of confused grapheme can be found using the fitness function which reflects the base stroke characteristics. The possibility of confused stroke existence can be measured by analyzing the boundary area of the base stroke. The result is merged with traditional OCR using score-probability converting. We achieved 68.1% error reduction for target grapheme pair errors by the proposed method and it means that 23.1 % total error is reduced View details
    An Example-based Prior Model for Text Image Super-resolution
    Jangkyun Park
    Jin Hyung Kim
    Proceedings of the eighth International Conference on Document Analysis and Recognition (ICDAR), Daejeon, South Korea, August 29 - September 1, 2005, pp. 374-378
    Preview abstract This paper presents a prior model for text image super-resolution in the Bayesian framework. In contrast to generic image super-resolution task, super-resolution of text images can be benefited from strong prior knowledge of the image class: firstly, low-resolution images are assumed to be generated from a high-resolution image by a sort of degradation which can be grasped through example pairs of the original and the corresponding degradation; secondly, text images are composed of two homogeneous regions, text and background regions. These properties were represented in a Markov random field (MRF) framework. Experiments showed that our model is more appropriate to text image super-resolution than the other prior models. View details
    Semi-supervised Kernel Regression Using Whitened Function Classes
    Matthias O. Franz
    Carl Edward Rasmussen
    Bernhard Schoelkopf
    Proceedings of Pattern Recognition, 26th DAGM Symposium, Tuebingen, Germany, August 30 - September 1, 2004, pp. 18-26
    Preview abstract The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique that results in an implicit whitening of the basis functions by penalizing directions in function space with a large prior variance. The regularization term is computed from unlabelled input data that characterizes the input distribution. Tests on two datasets using polynomial basis functions showed an improved average performance compared to standard ridge regression. View details