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Now showing 1 - 10 of 28

Automatic Restaurant Information and Keyword Extraction by Mining Blog Data for Chinese Restaurant Search
(2014-01-01)
Chou, Chien-Li
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Tsai, Min-Ho
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Chao, Chien-Ho
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Lin, Hsiao-Jung
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Chen, Hua-Tsung
Restaurant search and recommendation system is a very popular service in many countries. In those systems, most of the restaurant information such as restaurant name, address, phone number, and introduction are collected manually. In this paper, we propose a restaurant information extraction method which can automatically extract restaurant information from online reviews of restaurants in blogs. In addition, by calculating TFIDFs of words in blog posts, the hot keywords can be discovered and ranked. For restaurant search, users are allowed to search by keywords, areas, and/or extracted hot keywords. The experimental results show that the proposed method can achieve over 90 % average accuracy of hot keyword extraction and about 95 % mean average precision for restaurant search. In user study, the fact that the proposed system is more useful than Google search in restaurant search is presented.
A trajectory-based ball tracking framework with visual enrichment for broadcast baseball videos
(2008-01-01)
Chen, Hua-Tsung
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Chen, Hsuan-Sheng
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Hsiao, Ming-Ho
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Tsai, Wen-Jiin
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Lee, Suh-Yin
Pitching contents play the key role in the resultant victory or defeat in a baseball game. Utilizing the physical characteristic of ball motion, this paper presents a trajectory-based framework for automatic ball tracking and pitching evaluation in broadcast baseball videos. The task of ball detection and tracking in broadcast baseball videos is very challenging because in video frames, the noises may cause many ball-like objects, the ball size is small, and the ball may deform due to its high speed movement. To overcome these challenges, we first define a set of filters to prune most non-ball objects but retain the ball, even if it is deformed. In ball position prediction and trajectory extraction, we analyze the 2D distribution of ball candidates and exploit the characteristic that the ball trajectory presents in a near parabolic curve in video frames. Most of the non-qualified trajectories are pruned, which greatly improves the computational efficiency. The missed balls can also be recovered in the trajectory by applying the position prediction. The experiments of ball tracking on the testing sequences of JPB, MLB and CPBL captured from different TV channels show promising results. The ball tracking framework is able to extract the ball trajectory, superimposed on the video, and in near real-time provide visual enrichment before the next pitch coming up without specific cameras or equipments set up in the stadiums. It can also be utilized in strategy analysis and intelligence statistics for player training.
CIM: Community-Based Influence Maximization in Social Networks
(2014-04-01)
Chen, Yi-Cheng
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Zhu, Wen-Yuan
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Peng, Wen-Chih
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Lee, Wang-Chien
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Lee, Suh-Yin
Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.
A view-invariant and anti-reflection algorithm for car body extraction and color classification
(2013-08-01)
Gu, Hui-Zhen
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Lee, Suh-Yin
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資訊工程學系
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Department of Computer Science
This study proposes an intelligent algorithm with tri-state architecture for real-time car body extraction and color classification. The algorithm is capable of managing both the difficulties of viewpoint and light reflection. Because the influence of light reflection is significantly different on bright, dark, and colored cars, three different strategies are designed for various color categories to acquire a more intact car body. A SARM (Separating and Re-Merging) algorithm is proposed to separate the car body and the background, and recover the entire car body more completely. A robust selection algorithm is also performed to determine the correct color category and car body. Then, the color type of the vehicle is decided only by the pixels in the extracted car body. The experimental results show that the tri-state method can extract almost 90% of car body pixels from a car image. Over 98% of car images are distinguished correctly in their categories, and the average accuracy of the 10-color-type classification is higher than 93%. Furthermore, the computation load of the proposed method is light; therefore it is applicable for real-time systems.
Car model recognition by utilizing symmetric property to overcome severe pose variation
(2013-02-01)
Gu, Hui-Zhen
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Lee, Suh-Yin
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資訊工程學系
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Department of Computer Science
This paper presents a mirror morphing scheme to deal with the challenging pose variation problem in car model recognition. Conventionally, researchers adopt pose estimation techniques to overcome the pose problem, whereas it is difficult to obtain very accurate pose estimation. Moreover, slight deviation in pose estimation degrades the recognition performance dramatically. The mirror morphing technique utilizes the symmetric property of cars to normalize car images of any orientation into a typical view. Therefore, the pose error and center bias can be eliminated and satisfactory recognition performance can be obtained. To support mirror morphing, active shape model (ASM) is used to acquire car shape information. An effective pose and center estimation approach is also proposed to provide a good initialization for ASM. In experiments, our proposed car model recognition system can achieve very high recognition rate (> 95%) with very low probability of false alarm even when it is dealing with the severe pose problem in the cases of cars with similar shape and color.
HMM-based ball hitting event exploration system for broadcast baseball video
(2012-07-01)
Chen, Hua-Tsung
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Chou, Chien-Li
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Tsai, Wei-Chin
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Lee, Suh-Yin
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Lin, Bao-Shuh P.
With the dramatic growth of fandom population, a considerable amount of research efforts have been devoted to baseball video processing. However, little work focuses on the detailed follow-ups of ball hitting events. This paper proposes a HMM-based ball hitting event exploration system for broadcast baseball video. Utilizing the strictly-defined layout of the baseball field, the proposed system first detects the game-specific spatial patterns in the field, such as the field lines, the bases, the pitch mound, etc. Then, the play region the currently camera-focused region of the baseball field is identified for frame type classification. Since the temporal patterns of presenting the game progress follow a prototypical order, we consider the classified frame types as observation symbols and recognize ball hitting events using HMM. Experiments conducted on broadcast baseball video show encouraging results in frame type classification and ball hitting event recognition. Three practical applications, including highlight clip extraction by user-designated query, storyboard construction, and similar event retrieval, are introduced to address the applicability of our system. (C) 2012 Elsevier Inc. All rights reserved.
Efficient algorithms for influence maximization in social networks
(2012-12-01)
Chen, Yi-Cheng
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Peng, Wen-Chih
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Lee, Suh-Yin
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資訊工程學系
;
Department of Computer Science
In recent years, due to the surge in popularity of social-networking web sites, considerable interest has arisen regarding influence maximization in social networks. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. With a large-scale social network, the efficiency and practicability of such algorithms are critical. Although many recent studies have focused on the problem of influence maximization, these works in general are time-consuming when a social network is large-scale. In this paper, we propose two novel algorithms, CDH-Kcut and Community and Degree Heuristic on Kcut/SHRINK, to solve the influence maximization problem based on a realistic model. The algorithms utilize the community structure, which significantly decreases the number of candidates of influential nodes, to avoid information overlap. The experimental results on both synthetic and real datasets indicate that our algorithms not only significantly outperform the state-of-the-art algorithms in efficiency but also possess graceful scalability.
Social Photo Tagging Recommendation Using Community-Based Group Associations
(2012)
Chou, Chien-Li
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Chean, Yee-Choy
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Chen, Yi-Cheng
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Chen, Hua-Tsung
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Lee, Suh-Yin
In the social network, living photos occupy a large portion of web contents. For sharing a photo with the people appearing in that, users have to manually tag the people with their names, and the social network system links the photo to the people immediately. However, tagging the photos manually is a time-consuming task while people take thousands of photos in their daily life. Therefore, more and more researchers put their eyes on how to recommend tags for a photo. In this paper, our goal is to recommend tags for a query photo with one tagged face. We fuse the results of face recognition and the user's relationships obtained from social contexts. In addition, the Community-Based Group Associations, called CBGA, is proposed to discover the group associations among users through the community detection. Finally, the experimental evaluations show that the performance of photo tagging recommendation is improved by combining the face recognition and social relationship. Furthermore, the proposed framework achieves the high quality for social photo tagging recommendation.
Fast and memory efficient mining of high-utility itemsets from data streams: with and without negative item profits
(2011-09-01)
Li, Hua-Fu
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Huang, Hsin-Yun
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Lee, Suh-Yin
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資訊工程學系
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Department of Computer Science
Mining utility itemsets from data steams is one of the most interesting research issues in data mining and knowledge discovery. In this paper, two efficient sliding window-based algorithms, MHUI-BIT (Mining High-Utility Itemsets based on BITvector) and MHUI-TID (Mining High-Utility Itemsets based on TIDlist), are proposed for mining high-utility itemsets from data streams. Based on the sliding window-based framework of the proposed approaches, two effective representations of item information, Bitvector and TIDlist, and a lexicographical tree-based summary data structure, LexTree-2HTU, are developed to improve the efficiency of discovering high-utility itemsets with positive profits from data streams. Experimental results show that the proposed algorithms outperform than the existing approaches for discovering high-utility itemsets from data streams over sliding windows. Beside, we also propose the adapted approaches of algorithms MHUI-BIT and MHUI-TID in order to handle the case when we are interested in mining utility itemsets with negative item profits. Experiments show that the variants of algorithms MHUI-BIT and MHUI-TID are efficient approaches for mining high-utility itemsets with negative item profits over stream transaction-sensitive sliding windows.
Detecting changes in user-centered Music Query Streams
(2006)
Li, Hua-Fu
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Shan, Man-Kwan
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Lee, Suh-Yin
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資訊工程學系
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Department of Computer Science
In this paper, we propose an efficient algorithm, called MQS-change (changes of Music Query. Streams), to detect the changes of maximal melody structures in user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored in a new data structure MSC-list (a list of Music Structure Changes). Experiments show that MQS-change algorithm is an online, single-pass approach to detect the changes of music melody structures over continuous music query streams.

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