• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2479-2496
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• 2013

Increasing demand for Full High-Definition (FHD) video and Ultra High-Definition (UHD) video services has led to active research on high speed video processing. Widespread deployment of multi-core systems has accelerated studies on high resolution video processing based on parallelization of multimedia software. Even if parallelization of a specific decoding step may improve decoding performance partially, such partial parallelization may not result in sufficient performance improvement. Particularly, entropy decoding has often been considered separately from other decoding steps since the entropy decoding step could not be parallelized easily. In this paper, we propose a parallelization technique called Integrated Multi-Threaded Parallelization (IMTP) which takes parallelization of the entropy decoding step, with other decoding steps, into consideration in an integrated fashion. We used the Simultaneous Multi-Threading (SMT) technique with appropriate thread scheduling techniques to achieve the best performance for the entire decoding step. The speedup of the proposed IMTP method is up to 3.35 times faster with respect to the entire decoding time over a conventional decoding technique for H.264/AVC videos.

• Journal of Korea Multimedia Society
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• v.19 no.10
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• pp.1747-1758
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• 2016

In this paper, we propose a new fast Hough transform to improve the processing time and line detection of Hough transform that is widely used in various vision systems. First, for the fast processing time, we reduce the number of features by using multi-statistical methods and also reduce the dimension of angle through six separate directions. Next, for improving the line detection, we effectively detect the lines of various directions by designing the line detection method which detects line in proportion to the number of features in six separate directions. The proposed method was evaluated with previous methods and obtained the excellent results. The processing time was improved in about 20% to 50% and line detection was performed better in various directions than conventional methods with experimental images.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.446-448
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• 2021

In training a deep learning model, it is crucial to tune various hyperparameters and gain speed and accuracy. While hyperparameters that mathematically induce convergence impact training speed, system parameters that affect host-to-device transfer are also crucial. Therefore, it is important to properly tune and select parameters that influence the data loader as a system parameter in overall time acceleration. We propose an automated framework called Num Worker Tuner (NWT) to address this problem. This method finds the appropriate number of multi-processing subprocesses through the search space and accelerates the learning through the number of subprocesses. Furthermore, this method allows memory efficiency and speed-up by tuning the system-dependent parameter, the number of multi-process spawns.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.73-76
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• 2014

With the increasing popularity of multi-core processors, they have been adopted even in embedded systems. Under this circumstance many multimedia applications can be parallelized on multi-core platforms because they usually require heavy computations and extensive memory accesses. This paper proposes an efficient thread-level parallel implementation for color space conversion on multi-core CPU. Thread-level parallelism has been becoming very useful parallel processing paradigm especially on shared memory computing systems. In this work, it is exploited by allocating different input pixels to each thread for concurrent loop executions. For the performance evaluation, this paper evaluate the performace improvements for color conversion on multi-core processors based on the processing speed comparison between its serial implementation and parallel ones. The results shows that thread-level parallel implementations show the overall similar ratios of performance improvements regardless of different multi-cores.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.581-584
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• 2017

컴퓨터나 모바일 기기 사용자들이 필요한 자료나 정보 등을 검색하거나 콘텐츠가 있는 웹사이트를 찾는 경우, 흔히 사용하는 검색만으로는 어려움을 겪을 때가 존재한다. 이에 본 논문은 이러한 불편함을 최소화하기 위해 스마트폰 이용자가 원하는 콘텐츠를 찾고자 하는 경우, 분야별로 중요한 사이트들을 모아 해당 콘텐츠가 있는 사이트를 리스트로 제공해주는 어플리케이션을 개발하였다. 이는 사용자들에게 원하는 콘텐츠가 있는 사이트를 좀 더 쉽게 찾을 수 있도록 편리성을 줄 것이라 기대한다.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.991-1000
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• 2020

We propose a content-based image retrieval system that uses a combination of completed local binary pattern (CLBP) and color autocorrelogram. CLBP features are extracted on a multi-resolution multi-direction filtered domain of value component. Color autocorrelogram features are extracted in two dimensions of hue and saturation components. Experiment results revealed that the proposed method yields a lot of improvement when compared with the methods that use partial features employed in the proposed method. It is also superior to the conventional CLBP, the color autocorrelogram using R, G, and B components, and the multichannel decoded local binary pattern which is one of the latest methods.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1307-1317
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• 2018

To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally, the geometric center of the tracked areas is considered as the object's position. The proposed algorithm is evaluated on several classical videos. The experimental results show that the regression model weighted multi-templates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1995.11a
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• pp.79-88
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• 1995

Most works done so far have concentrated on developing data modeling techniques such as multi-level relation for data protection. These techniques, however, cannot be applied to practical area. This is because they require new queries or new architectures. In this paper, we propose a query processing technique for multi-level databases using horizontal partitioning and views, which does not need any change in database architecture and query language.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.163-164
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• 2011

본고에서는 Multi-class Jobs을 Dispatching system 에서 처리하는 경우, Cost performance 을 점근적으로 해석하는 과정에 대해 논의한다. 구체적으로, Job 할당 시스템은 Size-Interval 기반의 스케줄링 기법을 이용하고, Resource failure 에 대비하여 Job duplication 전략을 활용하는 것으로 가정 한다.