• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.8
• /
• pp.2881-2894
• /
• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 1999.06a
• /
• pp.83-88
• /
• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.3
• /
• pp.368-374
• /
• 2010

Image segmentation is the basic step in the field of the image processing for pattern recognition, environment recognition, and context analysis. The Otsu's automatic threshold selection, which determines the optimal threshold value to maximize the between class scatter using the distribution information of the normalized histogram of a image, is the famous method among the various image segmentation methods. For the automatic threshold selection proposed by Otsu, it is difficult to determine the optimal threshold value by considering the sub-region characteristic of the image because the Otsu's algorithm analyzes the global histogram of a image. In this paper, to alleviate this difficulty of Otsu's image segmentation algorithm and to improve image segmentation capability, the original image is divided into several sub-images by using context fuzzy c-means algorithm. The proposed fuzzy Otsu threshold algorithm is applied to the divided sub-images and the several threshold values are obtained.

• 한국HCI학회:학술대회논문집
• /
• 2006.02a
• /
• pp.926-930
• /
• 2006

Current image retrieval techniques have shortcomings that make it difficult to search for images based on a semantic understanding of what the image is about. Since an image is normally associated with multiple contexts (e.g. when and where a picture was taken,) the knowledge of these contexts can enhance the quantity of semantic understanding of an image. In this paper, we present a context-aware image retrieval system, which uses the context information to infer a kind of metadata for the captured images as well as images in different collections and databases. Experimental results show that using these kinds of information can not only significantly increase the retrieval accuracy in conventional content-based image retrieval systems but decrease the problems arise by manual annotation in text-based image retrieval systems as well.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.5
• /
• pp.674-677
• /
• 2020

The cultural background of users utilizing image search engines has a significant impact on the satisfaction of the search results. Therefore, it is important to analyze and understand the cultural context of images for more accurate image search. In this paper, we investigate how the cultural context of images can affect the performance of image classification. To this end, we first collected various types of images (e.g,. food, temple, etc.) with various cultural contexts (e.g., Korea, Japan, etc.) from web search engines. Afterwards, a deep transfer learning approach using VGG19 and MobileNetV2 pre-trained with ImageNet was adopted to learn the cultural features of the collected images. Through various experiments we show the performance of image classification can be differently affected according to the cultural context of images.

• Journal of Biomedical Engineering Research
• /
• v.40 no.5
• /
• pp.179-188
• /
• 2019

In ultrasound telemedicine, it is important to reduce the size of the data by compressing the ultrasound image when sending it. Ultrasound images can be divided into image context and other information consisting of patient ID, date, and several letters. Between them, ultrasound context is very important information for diagnosis and should be securely preserved as much as possible. In several previous papers, ultrasound compression methods were proposed to compress ultrasound context and other information into different compression parameters. This ultrasound compression method minimized the loss of ultrasound context while greatly compressing other information. This paper proposed the method of automatic segmentation of ultrasound context to overcome the limitation of the previously described ultrasound compression method. This algorithm was designed to robust for various ultrasound device and to enable real-time operation to maintain the benefits of ultrasound imaging machine. The operation time of extracting ultrasound context through the proposed segmentation method was measured, and it took 311.11 ms. In order to optimize the algorithm, the ultrasound context was segmented with down sampled input image. When the resolution of the input image was reduced by half, the computational time was 126.84 ms. When the resolution was reduced by one-third, it took 45.83 ms to segment the ultrasound context. As a result, we verified through experiments that the proposed method works in real time.

• Journal of Digital Convergence
• /
• v.10 no.4
• /
• pp.129-136
• /
• 2012

This study is presented on the corporate advertising strategy utilizing internet on corporate social responsibility in the context of the articles. To this end, Internet articles divided into positive and negative contexts, and has even attracted the attention of Internet articles and high /low and then separated into groups on ad attitudes and corporate image, the interaction effect was examined. Firstly, the Internet, a low level of condition of the article noted, in the context of a positive than a negative context, recall rates were higher. Second, the context of Internet articles and attention on the interaction effect between attitude toward advertising appeared. Third, the context of Internet articles and attention on the interaction effects between the corporate image appeared. Finally, the context of Internet articles and attention on competitive interactions between the corporate management was effective. Thus, the context of Internet articles based on the level of attention and context to determine the effect of advertising by consumer advertising awareness and favorable attitude toward corporate advertising and corporate image enhancement and competitiveness of business management can be an effective strategic plan suggests that.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.12
• /
• pp.2251-2256
• /
• 2007

In this paper, the wavelet transform based lossless image compression algorithm is proposed. The proposed algorithm transforms the input image using 9/7 ICFB and S+P filter, and eliminates the spacious correlation of the subband coefficients, applying the context modeling predictive technique based on the multi-resolution structure and the feedback of the prediction error. The prediction context exploits the subordination and direction property of the different level subband in the vertical, horizontal, and diagonal subband coefficients. The simulation result of the high frequency images such as PEPPERS, BOAT, and AIRPLANE shows that the proposed algorithm efficiently predicts the edge area of each multi-resolution subband.

• The Research Journal of the Costume Culture
• /
• v.12 no.2
• /
• pp.199-210
• /
• 2004

This study was intended to identify the differences in perception of image words and perception dimension of desired image according to sex variable for college students in a school context. Data were collected by questionnaires distributed to 443 college students from August to September of 2000. After eliminating incomplete questionnaires, 396 were analyzed. As results of factor analysis, 7 desired image factors were determined for male and female, respectively. On the basis of these results, major perception differences were identified according to sex variable. Differently-perceived image words were as fellows: For intellectual image, male relates it with sporty while female relates it with graceful; for masculine image, male relates it with mature while female relates it with sporty; and for feminine image, male relates it with sexy and graceful while female relates it modest, pure, and cute. Multidimensional scaling was employed to determine the perception dimension of desired image. Two dimensions were accepted to interpret the results for both sex, respectively. 'Feminine-masculine' and 'conspicuous-plain' were criteria for male students, while 'unnatural-natural' and 'feminine-masculine' were criteria for female students.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2003.10a
• /
• pp.397-402
• /
• 2003

This study is aimed to improve the quality of image fusion results through shadow effects correction. For this, shadow effects correction algorithm is proposed and visual comparisons have been made to estimate the quality of image fusion results. The following four steps have been performed to improve the image fusion qualify First, the shadow regions of satellite image are precisely located. Subsequently, segmentation of context regions is manually implemented for accurate correction. Next step, to calculate correction factor we compared the context region with the same non-shadow context region. Finally, image fusion is implemented using collected images. The result presented here helps to accurately extract and interpret geo-spatial information from satellite imagery.