• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3745-3761
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• 2020

Low-rank representation methods already achieve many applications in the image reconstruction. However, for high-gradient image patches with rich texture details and strong edge information, it is difficult to find sufficient similar patches. Existing low-rank representation methods usually destroy image critical details and fail to preserve edge structure. In order to promote the performance, a new representation-based image super-resolution reconstruction method is proposed, which combines gradient domain guided image filter with the structure-constrained low-rank representation so as to enhance image details as well as reveal the intrinsic structure of an input image. Firstly, we extract the gradient domain guided filter of each atom in high resolution dictionary in order to acquire high-frequency prior information. Secondly, this prior information is taken as a structure constraint and introduced into the low-rank representation framework to develop a new model so as to maintain the edges of reconstructed image. Thirdly, the approximate optimal solution of the model is solved through alternating direction method of multipliers. After that, experiments are performed and results show that the proposed algorithm has higher performances than conventional state-of-the-art algorithms in both quantitative and qualitative aspects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권3호
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• pp.1140-1154
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• 2015

Multiple Description (MD) coding is a promising alternative for the robust transmission of information over error-prone channels. Lattice vector quantization (LVQ) is a significant version of MD techniques to design an MD image coder. However, different from the traditional 2D texture image, the 3D depth image has its own special characteristics, which should be taken into account for efficient compression. In this paper, an optimized MDLVQ scheme is proposed in view of the characteristics of 3D depth image. First, due to the sparsity of depth image, the image blocks can be classified into edge blocks and smooth blocks, which are encoded by different modes. Furthermore, according to the boundary contents in edge blocks, the step size of LVQ can be regulated adaptively for each block. Experimental results validate the effectiveness of the proposed scheme, which show better rate distortion performance compared with the conventional MDLVQ.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권12호
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• pp.3149-3165
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• 2013

Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

• 방송공학회논문지
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• 제18권4호
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• pp.550-558
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• 2013

가시광 영상과 원적외선 영상은 각각 질감 정보와 열 정보를 가지므로 서로 다른 정보를 표현한다. 그러므로 가시광 영상 개선을 위해 가시광 영상의 정보만을 이용하는 것보다 가시광 영상에서 존재하지 않는 원적외선 영상의 열 정보를 이용하는 것이 보다 좋은 결과를 얻을 수 있다. 본 논문에서는 원적외선 영상을 이용한 효과적인 가시광 영상 개선을 위해 가시광 영상에서 개선이 필요한 정도에 따라 가중치 맵을 만든다. 가중치 맵은 채도와 밝기를 이용하여 계산하며 원적외선 영상에서 열 정보를 고려하여 값을 조정한다. 마지막으로 조정된 가중치 맵을 이용하여 원적외선 영상의 정보와 가시광 영상의 정보를 융합함으로써 두 영상의 정보를 효과적으로 포함한 결과 영상을 생성한다. 실험결과에서는 가시광 영상에서 개선이 필요한 영역을 원적외선 영상 정보와의 융합으로 원본의 가시광 영상보다 향상된 결과를 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권1호
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• pp.211-227
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• 2011

Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.1078-1099
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• 2021

Traditional image steganography hides secret information by embedding, which inevitably leaves modification traces and is easy to be detected by steganography analysis tools. Since coverless steganography can effectively resist steganalysis, it has become a hotspot in information hiding research recently. Most coverless image steganography (CIS) methods are based on mapping rules, which not only exposes the vulnerability to geometric attacks, but also are less secure due to the revelation of mapping rules. To address the above issues, we introduced camouflage images for steganography instead of directly sending stego-image, which further improves the security performance and information hiding ability of steganography scheme. In particular, based on the different sub-features of stego-image and potential camouflage images, we try to find a larger similarity between them so as to achieve the reversible steganography. Specifically, based on the existing CIS mapping algorithm, we first can establish the correlation between stego-image and secret information and then transmit the camouflage images, which are obtained by reversible sub-feature retrieval algorithm. The received camouflage image can be used to reverse retrieve the stego-image in a public image database. Finally, we can use the same mapping rules to restore secret information. Extensive experimental results demonstrate the better robustness and security of the proposed approach in comparison to state-of-art CIS methods, especially in the robustness of geometric attacks.

• Journal of Information Processing Systems
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• 제17권6호
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• pp.1138-1156
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• 2021

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

• 한국정보통신학회논문지
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• 제10권12호
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• pp.2343-2348
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• 2006

영상처리에서 영상확대기법은 기본적인 처리기법으로 일반적으로 사용되는 기법은 보간법(최근접이웃, 양선형, 3차회선 보간법)이다. 그러나 이러한 보간법은 영상확대시 블록화 현상이나 몽롱화현상과 같은 영상의 손실이 발생하거나 계산량이 많아 처리시간이 길게 나타났다. 따라서 본 논문에서는 입력영상의 부대역정보인 에지정보를 이용하여 기존의 확대기법을 개선하고자 한다. 에지정보를 추출하기 위하여 이웃한 화소들을 이용하지 않고 전체영상을 이용하여 블록화현상이 발생되지 않았다. 그리고 에지가 결여되어 나타나는 몽롱화현상을 제거하기 위하여 검출된 에지정보를 강조시켰다. 실험 결과, 제안된 기법은 기존의 확대기법보다 처리시간을 줄일 수 있었으며, PSNR과 상관계수에서도 성능이 뛰어나 블록화나 몽롱화현상과 같은 문제점을 해결하였다.

• 한국문헌정보학회지
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• 제49권4호
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• pp.99-124
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• 2015

As the improvement of digital technologies increases the use of images from various fields, the domain of image retrieval has evolved and become a growing topic of research in the Library and Information Science field. The purpose of this study is to identify the knowledge structure of the image retrieval domain by using the author co-citation analysis and author bibliographic coupling as analytical tools in order to understand the domain's past and present. The data set for this study is 245 articles with 8,031 cited articles in the field of image retrieval from 1998 to 2013, from the Web of Science citation database. According to the results of author co-citation analysis for the past of the image retrieval domain, our findings demonstrate that the intellectual structure of image retrieval in the LIS field consists of predominantly user-oriented approaches, but also includes some areas influenced by the CBIR area. More specifically, the user-oriented approach contains six specific areas which include image needs, information seeking, image needs and search behavior, image indexing and access, indexing of image collection, and web image search. On the other hand, for CBIR approaches, it contains feature-based image indexing, shape-based indexing, and IR & CBIR. The recent trends of image retrieval based on the results from author bibliographic coupling analysis show that the domain is expanding to emerging areas of medical images, multimedia, ontology- and tag-based indexing which thus reflects a new paradigm of information environment.

• 한국컴퓨터정보학회논문지
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• 제20권7호
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• pp.91-97
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• 2015

In this paper, we designed a emergency medical image communication system EMICS added concept of emergency medical image to the existing emergency medical information system based on DICOM. Also we suggested a emergency medical image object EMISPS of EMICS. Using EMICS, the emergency medical technician can work together with emergency doctor. Therefore the patient can take more stable care than existing emergency medical information system. Using EMISPS, the emergency medical technician can get exact situation information of the patient.