• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.38 no.1
• /
• pp.46-57
• /
• 2001

To access the multimedia data, such as video data with temporal properties, the content-based image retrieval technique is required. Moreover, one of the basic techniques for content-based image retrieval is an extraction of representative key-frames. Not only did we implement this method, but also by analyzing the video data, we have proven the proposed method to be both effective and accurate. In addition, this method is expected to solve the real world problem of building video databases, as it is very useful in building an index.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.139-140
• /
• 2012

This paper proposes a Video Browsing Service that provides both the video content retrieval and the video browsing by the real-time user interface on Web. For the scene segmentation and key frame extraction of video sequence, we proposes an efficient scene change detection method that combine the RGB color histogram with the ${\chi}2$ histogram.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.6
• /
• pp.1129-1133
• /
• 2006

In this thesis, we propose a semantics-based video retrieval system which supports semantics-retrieval for various users of massive video data. Proposed system automatically processes the extraction of contents information which video data has and retrieval process using agent which integrate annotation-based retrieval and feature-based retrieval. From experiment, the designed and implemented system shows increase of recall rate and precision rate for video data scene retrieval in performance assessment.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.4
• /
• pp.110-122
• /
• 2014

As multimedia technologies develop, three-dimensional (3D) technologies are attracting increasing attention from researchers. In particular, video quality assessment (VQA) has become a critical issue in stereoscopic image/video processing applications. Furthermore, a human visual system (HVS) could play an important role in the measurement of stereoscopic video quality, yet existing VQA methods have done little to develop a HVS for stereoscopic video. We seek to amend this by proposing a 3D visual attention (3DVA) model which simulates the HVS for stereoscopic video by combining multiple perceptual stimuli such as depth, motion, color, intensity, and orientation contrast. We utilize this 3DVA model for pooling on significant regions of very poor video quality, and we propose no-reference (NR) stereoscopic VQA (SVQA) method. We validated the proposed SVQA method using subjective test scores from our results and those reported by others. Our approach yields high correlation with the measured mean opinion score (MOS) as well as consistent performance in asymmetric coding conditions. Additionally, the 3DVA model is used to extract information for the region-of-interest (ROI). Subjective evaluations of the extracted ROI indicate that the 3DVA-based ROI extraction outperforms the other compared extraction methods using spatial or/and temporal terms.

• Journal of KIISE:Software and Applications
• /
• v.29 no.4
• /
• pp.235-247
• /
• 2002

For an efficient indexing and retrieval of digital video data, research on video caption extraction and recognition is required. This paper proposes methods for extracting artificial captions from video data and enhancing their image quality for an accurate Hangul and English character recognition. In the proposed methods, we first find locations of beginning and ending frames of the same caption contents and combine those multiple frames in each group by logical operation to remove background noises. During this process an evaluation is performed for detecting the integrated results with different caption images. After the multiple video frames are integrated, four different image enhancement techniques are applied to the image: resolution enhancement, contrast enhancement, stroke-based binarization, and morphological smoothing operations. By applying these operations to the video frames we can even improve the image quality of phonemes with complex strokes. Finding the beginning and ending locations of the frames with the same caption contents can be effectively used for the digital video indexing and browsing. We have tested the proposed methods with the video caption images containing both Hangul and English characters from cinema, and obtained the improved results of the character recognition.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.11C
• /
• pp.929-939
• /
• 2010

The growth of multimedia technology forces the development of video detection for large database management and illegal copy detection. To meet this demand, this paper proposes a fast video detection method to apply to a large database. The fast video detection algorithm uses spatial features using the gray value distribution from frames and temporal features using the temporal similarity map. We form the video signature using the extracted spatial feature and temporal feature, and carry out a stepwise matching method. The performance was evaluated by accuracy, extraction and matching time, and signature size using the original videos and their modified versions such as brightness change, lossy compression, text/logo overlay. We show empirical parameter selection and the experimental results for the simple matching method using only spatial feature and compare the results with existing algorithms. According to the experimental results, the proposed method has good performance in accuracy, processing time, and signature size. Therefore, the proposed fast detection algorithm is suitable for video detection with the large database.

• Journal of Internet Computing and Services
• /
• v.11 no.1
• /
• pp.85-102
• /
• 2010

In this paper, we propose a novel method of analyzing video and representing the relationship among characters based on their contexts in the video sequences, namely Character-Net. As a huge amount of video contents is generated even in a single day, the searching and summarizing technologies of the contents have also been issued. Thereby, a number of researches have been proposed related to extracting semantic information of video or scenes. Generally stories of video, such as TV serial or commercial movies, are made progress with characters. Accordingly, the relationship between the characters and their contexts should be identified to summarize video. To deal with these issues, we propose Character-Net supporting the extraction of major characters in video. We first identify characters appeared in a group of video shots and subsequently extract the speaker and listeners in the shots. Finally, the characters are represented by a form of a network with graphs presenting the relationship among them. We present empirical experiments to demonstrate Character-Net and evaluate performance of extracting major characters.

• Journal of Information Processing Systems
• /
• v.17 no.3
• /
• pp.556-570
• /
• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.911-914
• /
• 1998

In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details shouuld be coded in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological laplacian operator and modified post-it transform is very efficient for improving quality of the reconstructed images.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.835-838
• /
• 2000

Popular methods for extracting a text region in video images are in general based on analysis of a whole image such as merge and split method, and comparison of two frames. Thus, they take long computing time due to the use of a whole image. Therefore, this paper suggests the faster method of extracting a text region without processing a whole image. The proposed method uses line sampling methods, FFT and neural networks in order to extract texts in real time. In general, text areas are found in the higher frequency domain, thus, can be characterized using FFT The candidate text areas can be thus found by applying the higher frequency characteristics to neural network. Therefore, the final text area is extracted by verifying the candidate areas. Experimental results show a perfect candidate extraction rate and about 92% text extraction rate. The strength of the proposed algorithm is its simplicity, real-time processing by not processing the entire image, and fast skipping of the images that do not contain a text.