• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.21-23
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• 2006

Due to development of Internet network environments and data compression techniques, the size and amount of multimedia data has greatly increased. They are compressed before transmission or storage. Dealing with these compressed data such as video retrieval or indexing requires the decoding procedure most of the time. In video retrieval and indexing a color histogram is one of the most frequently used tools. We propose a novel scheme for extracting color histograms from images transformed into the compressed domain using $8{times}8$ DCT(Discrete Cosine Transform). In this scheme an averaged version of original image is obtained by filtering DCT coefficients with a filter we destined.

• International Journal of Contents
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• v.12 no.1
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• pp.1-5
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• 2016

To manipulate large video contents, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-wise user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm that extracts key frames using color histograms and matches the video sequences using edge features. To effectively match video sequences with a low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with real sequence show that the proposed video sequence matching algorithm using edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

• 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.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2006.06a
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• pp.45-48
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• 2006

This paper presents an approach to visual information based temporal indexing of video sequences. The objective of this work is the integration of an automatic face detection and a matching system for video indexing. The face detection is done using color information. The matching stage is based on the Principal Component Analysis (PCA) followed by the Minimax Probability Machine (MPM). Using PCA one feature vector is calculated for each face which is detected at the previous stage from the video sequence and MPM is applied to these feature vectors for matching with the training faces which are manually indexed after extracting from video sequences. The integration of the two stages gives good results. The rate of 86.3% correctly classified frames shows the efficiency of our system.

• Journal of Korea Multimedia Society
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• v.1 no.2
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• pp.173-182
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• 1998

As a result of remarkable developments in multimedia technology, the image database system that can efficiently retrieve image data becomes a core technology of information-oriented society. In this paper, we proposed the 2-phase Image Retrieval System considering both color and shape information as the method of image features extraction for content-based image data retrieval. At the first level, to get color information, with improving and extending the indexing method using color distribution characteristic suggested by Striker et al., i.e. the indexing method considering local color distribution characteristics, the system roughly classifies images through the improved method. At the second level, the system finally retrieves the most similar image from the image queried by the user using the shape information about the image groups classified at the first level. To extract the shape information, we use the Improved Moment Invariants (IMI) that manipulates only the pixels on the edges of objects in order to overcome two main problems of the existing Moment Invariant methods large amount of processing and rotation sensitiveness which can frequently be seen in the Directive Histogram Intersection technique suggested by Jain et al. Experiments have been conducted on 300 automobile images. And we could obtain the more improved results through the comparative test with other methods.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.3
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• pp.23-34
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• 2001

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.74-81
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• 2005

As the multimedia data increases, various scene change detection algorithms for video indexing and sequence matching have been proposed to efficiently manage and utilize digital media. In this paper, we propose a robust scene change detection algorithm for video sequences with abrupt luminance variations. To improve the accuracy and to reduce the computational complexity of video indexing with abrupt luminance variations, the proposed algorithm utilizes edge features as well as color features, which yields a remarkably better performance than conventional algorithms. In the proposed algorithm first we extract the candidate shot boundaries using color histograms and then determine using edge matching and luminance compensation if they are shot boundaries or luminance changes. If the scene contains trivial brightness variations, the edge matching and luminance compensation are performed only for shot boundaries. In experimental results, the proposed method gives remarkably a high performance and efficiency than the conventional methods with the similar computational complexity.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.215-217
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• 2008

The most widely used method for searching contents of mot ion picture compares contents by extracted cuts. The cut extract ion methods, such as CHD(Color Histogram Difference) or ECR(Edge Change Ratio), are very weak at modifications such as cropping, resizing and low bit rate. The suggested method uses audio contents for indexing and searching to make search be robust against these modification. Scenes of audio contents are extracted for modification-robust search. And based on these scenes, make spectral powers binary on each frequency bin. in the time-frequency domain. The suggested method shows failure rate less than 1% on the false positive error and the true negative error to the modified(using cropping, clipping, row bit rate, addtive frame) contents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.535-540
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• 2006

This paper presented an effective strategy to extract the buildings and to reconstruct 3-D buildings using high-resolution multispectral stereo satellite images. Proposed scheme contained three major steps: building enhancement and segmentation using both BDT (Background Discriminant Transformation) and ISODATA algorithm, conjugate building identification using the object matching with Hausdorff distance and color indexing, and 3-D building reconstruction using photogrammetric techniques. IKONOS multispectral stereo images were used to evaluate the scheme. As a result, the BDT technique was verified as an effective tool for enhancing building areas since BDT suppressed the dominance of background to enhance the building as a non-background. In building recognition, color information itself was not enough to identify the conjugate building pairs since most buildings are composed of similar materials such as concrete. When both Hausdorff distance for edge information and color indexing for color information were combined, most segmented buildings in the stereo images were correctly identified. Finally, 3-D building models were successfully generated using the space intersection by the forward RFM (Rational Function Model).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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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.