• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.1
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• pp.127-135
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• 2009

This paper presents a new technique for corner shape based object retrieval from a database. The proposed feature matrix consists of values obtained through a neighborhood operation of detected corners. This results in a significant small size feature matrix compared to the algorithms using color features and thus is computationally very efficient. The corners have been extracted by finding the intersections of the detected lines found using Hough transform. As the affine transformations preserve the co-linearity of points on a line and their intersection properties, the resulting corner features for image retrieval are robust to affine transformations. Furthermore, the corner features are invariant to noise. It is considered that the proposed algorithm will produce good results in combination with other algorithms in a way of incremental verification for similarity.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.2
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• pp.219-226
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• 2017

This paper addresses the effective and quantitative image DB construction for the development of front looking camera systems. The automotive industry has expanded the capability of front camera solutions that will help ADAS(Advanced Driver Assistance System) applications targeting Euro NCAP function requirements. These safety functions include AEB(Autonomous Emergency Braking), TSR(Traffic Signal Recognition), LDW(Lane Departure Warning) and FCW(Forward Collision Warning). In order to guarantee real road safety performance, the driving image DB logged under various real road conditions should be used to train core object classifiers and verify the function performance of the camera system. However, the driving image DB would entail an invalid and time consuming task without proper guidelines. The standard working procedures and design factors required for each step to build an effective image DB for reliable automotive front looking camera systems are proposed.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.605-608
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• 1999

In this paper, we proposed several scene analysis algorithms. These algorithms using image difference and histogram operate on the sequence of DC coefficient which is extracted from Motion JPEG or MPEG without full-frame decompression. Since DC sequence has the most information of full frame while it has reduced data. Experimental results show less than 1/64 of full frame analysing complexity and exactly analyze scene changes and extract key frames.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.701-708
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• 2004

Existing contents-based video retrieval systems search by using a single method such as annotation-based or feature-based retrieval. Hence, it not only shows low search efficiency, but also requires many efforts to provide system administrator or annotator with a perfect automatic processing. Tn this paper, we propose an agent-based, and automatic and unified semantics-based video retrieval system, which support various semantics-retrieval of the massive video data by integrating the feature-based retrieval and the annotation-based retrieval. The indexing agent embodies the semantics about annotation of extracted key frames by analyzing a fundamental query of a user and by selecting a key-frame image that is ed by a query. Also, a key frame selected by user takes a query image of the feature-based retrieval and the indexing agent searches and displays the most similar key-frame images after comparing query images with key frames in the database by using the color-multiple-partition histogram techniques. Furthermore, it is shown that the performance of the proposed system can be significantly improved.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.127-137
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• 2006

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic- based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency and requires many efforts of system administrator or annotator form less perfect automatic processing. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the automatic indexing agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. Therefore, we propose the system that can heighten retrieval efficiency of video data through semantic-based retrieval.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.121-138
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• 2017

As information users seek multimedia with a wide variety of information needs, information environments for multimedia have been developed drastically. More specifically, as seeking multimedia with emotional access points has been popular, the needs for indexing in terms of abstract concepts including emotions have grown. This study aims to analyze the index terms extracted from Getty Image Bank. Five basic emotion terms, which are sadness, love, horror, happiness, anger, were used when collected the indexing terms. A total 22,675 index terms were used for this study. The data are three sets; entire emotion, positive emotion, and negative emotion. For these three data sets, co-word occurrence matrices were created and visualized in weighted network with PNNC clusters. The entire emotion network demonstrates three clusters and 20 sub-clusters. On the other hand, positive emotion network and negative emotion network show 10 clusters, respectively. The results point out three elements for next generation of multimedia retrieval: (1) the analysis on index terms for emotions shown in people on image, (2) the relationship between connotative term and denotative term and possibility for inferring connotative terms from denotative terms using the relationship, and (3) the significance of thesaurus on connotative term in order to expand related terms or synonyms for better access points.

• Journal of Korea Multimedia Society
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• v.10 no.5
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• pp.559-568
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• 2007

Anchor-person scene detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper proposes an efficient algorithm extracting anchor ranges that exist in sports news video for unit structuring of sports news. To detect anchor person scenes, first, anchor person candidate scene is decided by DCT coefficients and motion vector information in the MPEG4 compressed video. Then, from the candidate anchor scenes, image processing method is utilized to classify the news video into anchor-person scenes and non-anchor(sports) scenes. The proposed scheme achieves a mean precision and recall of 98% in the anchor-person scenes detection experiment.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.526-539
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• 2012

This paper proposes a high capacity data hiding method using high frequence secret data indexing algorithm. Many novel data hiding methods based on LSB and PVD methods were presented to enlarge hiding capacity and provide an imperceptible quality. In this paper, first, calculating data iteration frequency of the secret message and make up the high frequency data index matrix (HFDT) using high frequence data's location information. Next, HFDT uses to that data hiding process on the cover image and recovering process on the stego image. The experimental results demonstrate the efficiency of the proposed high frequency secret data indexing method. For the data hiding method, experiments are conducted for four cases: 2 pattern secret data (2PD), 4 pattern secret data (4PD), 8 pattern secret data (8PD) and higher pattern secret data (HPD). When comparing the proposed method with other data hiding methods, for the HPD case, the results show that the proposed method has a good PSNR and more capacity, and for the other case, the results show that the proposed method has a higher PSNR and larger capacity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11A
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• pp.870-881
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• 2009

The rapid development of healthcare information management for 3D medical digital library, 3D PACS and 3D medical diagnosis has addressed security issues with medical IT technology. This paper presents multiple 3D medical image data for protection, authentication, indexing and diagnosis information hiding applied to healthcare information management. The proposed scheme based on POCS watermarking embeds the robust watermark for doctor's digital signature and information retrieval indexing key to the distribution of vertex curvedness and embeds the fragile watermark for diagnosis information and authentication reference message to the distance difference of vertex. The multiple embedding process designs three convex sets for robustness, fragileness and invisibility and projects 3D medical image data onto three convex sets alternatively and iteratively. Experimental results confirmed that the proposed scheme has the robustness and fragileness to various 3D geometric and mesh modifiers at once.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.328-331
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• 2000

In this paper , we propose a texture feature extraction method of reduce the massive computational time on extracting texture, features of large sized medical such as MRI, CT-scan , and an index structure, called GLTFT, to speed up the retrieval performance. For these, the original image is transformed into a compressed image by Wavelet transform , and textural features such as contrast, energy, entropy, and homogeneity of the compressed image is extracted by using GLCM(Gray Level Co-occurrence Metrix) . The proposed index structure is organized by using the textural features. The processing in compressed domain can give the solution of storage space and the reduction of computational time of feature extracting . And , by GLTFT index structure, image retrieval performance can be expected to be improved by reducing the retrieval range . Our experiment on 270 MRIs as image database shows that shows that such expectation can be got.