• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.1-8
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• 2009

There are a lot of influences, such as location of camera, luminosity, brightness, and direction of light, which affect the performance of 2-dimensional image recognition. This paper suggests an adaptive method for face-image recognition in noisy environments using evolvable filtering and feature extraction which uses one sample image from camera. This suggested method consists of two main parts. One is the environmental-adjustment module which determines optimum sets of filters, filter parameters, and dimensions of features by using "steady state genetic algorithm". The other another part is for face recognition module which performs recognition of face-image using the previous results. In the processing, we used Gabor wavelet for extracting features in the images and k-Nearest Neighbor method for the classification. For testing of the adaptive face recognition method, we tested the adaptive method in the brightness noise, in the impulse noise and in the composite noise and verified that the adaptive method protects face recognition-rate's rapidly decrease which can be occurred generally in the noisy environments.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.823-834
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• 2004

In this research, the similarity search algorithms are provided for large video data streams. A video stream that consists of a number of frames can be expressed by a sequence in the multidimensional data space, by representing each frame with a multidimensional vector By analyzing various characteristics of the sequence, it is partitioned into multiple video segments and clusters which are represented by hyper-rectangles. Using the hyper-rectangles of video segments and clusters, similarity functions between two video streams are defined, and two similarity search algorithms are proposed based on the similarity functions algorithms by hyper-rectangles and by representative frames. The former is an algorithm that guarantees the correctness while the latter focuses on the efficiency with a slight sacrifice of the correctness Experiments on different types of video streams and synthetically generated stream data show the strength of our proposed algorithms.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.452-472
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• 2005

We propose XIR-Branching, a novel method for processing partial match queries on heterogeneous XML documents using information retrieval(IR) techniques and novel instance join techniques. A partial match query is defined as the one having the descendent-or-self axis '//' in its path expression. In its general form, a partial match query has branch predicates forming branching paths. The objective of XIR-Branching is to efficiently support this type of queries for large-scale documents of heterogeneous schemas. XIR-Branching has its basis on the conventional schema-level methods using relational tables(e.g., XRel, XParent, XIR-Linear[21]) and significantly improves their efficiency and scalability using two techniques: an inverted index technique and a novel prefix match join. The former supports linear path expressions as the method used in XIR-Linear[21]. The latter supports branching path expressions, and allows for finding the result nodes more efficiently than containment joins used in the conventional methods. XIR-Linear shows the efficiency for linear path expressions, but does not handle branching path expressions. However, we have to handle branching path expressions for querying more in detail and general. The paper presents a novel method for handling branching path expressions. XIR-Branching reduces a candidate set for a query as a schema-level method and then, efficiently finds a final result set by using a novel prefix match join as an instance-level method. We compare the efficiency and scalability of XIR-Branching with those of XRel and XParent using XML documents crawled from the Internet. The results show that XIR-Branching is more efficient than both XRel and XParent by several orders of magnitude for linear path expressions, and by several factors for branching path expressions.