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- Click-through data (2)
- Image search (2)
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- Concept detection (1)
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- Error propagation (1)
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- Identifying potential customers (1)
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- Input sanitization (1)
- Instance search (1)
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- Lexicon construction (1)
- Microblog marketing (1)
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- Near-duplicate video (1)
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Articles 1 - 13 of 13
Full-Text Articles in Physical Sciences and Mathematics
A Simple Integration Of Social Relationship And Text Data For Identifying Potential Customers In Microblogging, Guansong Pang, Shengyi Jiang, Dongyi Chen
A Simple Integration Of Social Relationship And Text Data For Identifying Potential Customers In Microblogging, Guansong Pang, Shengyi Jiang, Dongyi Chen
Research Collection School Of Computing and Information Systems
Identifying potential customers among a huge number of users in microblogging is a fundamental problem for microblog marketing. One challenge in potential customer detection in microblogging is how to generate an accurate characteristic description for users, i.e., user profile generation. Intuitively, the preference of a user’s friends (i.e., the person followed by the user in microblogging) is of great importance to capture the characteristic of the user. Also, a user’s self-defined tags are often concise and accurate carriers for the user’s interests. In this paper, for identifying potential customers in microblogging, we propose a method to generate user profiles via …
Adaptive Computer‐Generated Forces For Simulator‐Based Training, Expert Systems With Applications, Teck-Hou Teng, Ah-Hwee Tan, Loo-Nin Teow
Adaptive Computer‐Generated Forces For Simulator‐Based Training, Expert Systems With Applications, Teck-Hou Teng, Ah-Hwee Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Simulator-based training is in constant pursuit of increasing level of realism. The transition from doctrine-driven computer-generated forces (CGF) to adaptive CGF represents one such effort. The use of doctrine-driven CGF is fraught with challenges such as modeling of complex expert knowledge and adapting to the trainees’ progress in real time. Therefore, this paper reports on how the use of adaptive CGF can overcome these challenges. Using a self-organizing neural network to implement the adaptive CGF, air combat maneuvering strategies are learned incrementally and generalized in real time. The state space and action space are extracted from the same hierarchical doctrine …
Error Recovered Hierarchical Classification, Shiai Zhu, Xiao-Yong Wei, Chong-Wah Ngo
Error Recovered Hierarchical Classification, Shiai Zhu, Xiao-Yong Wei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Hierarchical classification (HC) is a popular and efficient way for detecting the semantic concepts from the images. However, the conventional HC, which always selects the branch with the highest classification response to go on, has the risk of propagating serious errors from higher levels of the hierarchy to the lower levels. We argue that the highestresponse-first strategy is too arbitrary, because the candidate nodes are considered individually which ignores the semantic relationship among them. In this paper, we propose a novel method for HC, which is able to utilize the semantic relationship among candidate nodes and their children to recover …
Image Search By Graph-Based Label Propagation With Image Representation From Dnn, Yingwei Pan, Yao Ting, Kuiyuan Yang, Houqiang Li, Chong-Wah Ngo, Jingdong Wang, Tao Mei
Image Search By Graph-Based Label Propagation With Image Representation From Dnn, Yingwei Pan, Yao Ting, Kuiyuan Yang, Houqiang Li, Chong-Wah Ngo, Jingdong Wang, Tao Mei
Research Collection School Of Computing and Information Systems
Our objective is to estimate the relevance of an image to a query for image search purposes. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of bridging the gap between semantic textual queries as well as users’ search intents and image visual content. Image search engines therefore primarily rely on static and textual features. Visual features are mainly used to identify potentially useful recurrent patterns or relevant training examples for complementing search by image reranking. Second, image rankers are trained on query-image pairs labeled by human experts, making the …
Annotation For Free: Video Tagging By Mining User Search Behavior, Yao Ting, Tao Mei, Chong-Wah Ngo, Shipeng Li
Annotation For Free: Video Tagging By Mining User Search Behavior, Yao Ting, Tao Mei, Chong-Wah Ngo, Shipeng Li
Research Collection School Of Computing and Information Systems
The problem of tagging is mostly considered from the perspectives of machine learning and data-driven philosophy. A fundamental issue that underlies the success of these approaches is the visual similarity, ranging from the nearest neighbor search to manifold learning, to identify similar instances of an example for tag completion. The need to searching for millions of visual examples in high-dimensional feature space, however, makes the task computationally expensive. Moreover, the results can suffer from robustness problem, when the underlying data, such as online videos, are rich of semantics and the similarity is difficult to be learnt from low-level features. This …
Web-Scale Near-Duplicate Search: Techniques And Applications, Chong-Wah Ngo, Changsheng Xu, Wessel Kraaij, Abdulmotaleb El Saddik
Web-Scale Near-Duplicate Search: Techniques And Applications, Chong-Wah Ngo, Changsheng Xu, Wessel Kraaij, Abdulmotaleb El Saddik
Research Collection School Of Computing and Information Systems
This paper presents some of the most recent advances in the research on Web-scale near-duplicate search and explores the potential for bringing this research a substantial step further. It contains high-quality contributions addressing various aspects of the Web-scale near-duplicate search problem in a number of relevant domains. The topics range from feature representation, matching, and indexing from different novel aspects to the adaptation of current technologies for mobile media search and photo archaeology mining.
Near-Duplicate Video Retrieval: Current Research And Future Trends, Jiajun Liu, Zi Huang, Hongyun Cai, Heng Tao Shen, Chong-Wah Ngo, Wei Wang
Near-Duplicate Video Retrieval: Current Research And Future Trends, Jiajun Liu, Zi Huang, Hongyun Cai, Heng Tao Shen, Chong-Wah Ngo, Wei Wang
Research Collection School Of Computing and Information Systems
The exponential growth of online videos, along with increasing user involvement in video-related activities, has been observed as a constant phenomenon during the last decade. User's time spent on video capturing, editing, uploading, searching, and viewing has boosted to an unprecedented level. The massive publishing and sharing of videos has given rise to the existence of an already large amount of near-duplicate content. This imposes urgent demands on near-duplicate video retrieval as a key role in novel tasks such as video search, video copyright protection, video recommendation, and many more. Driven by its significance, near-duplicate video retrieval has recently attracted …
Click-Boosting Random Walk For Image Search Reranking, Xiaopeng Yang, Yongdong Zhang, Ting Yao, Zheng-Jun Zha, Chong-Wah Ngo
Click-Boosting Random Walk For Image Search Reranking, Xiaopeng Yang, Yongdong Zhang, Ting Yao, Zheng-Jun Zha, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Image reranking is an effective way for improving the retrieval performance of keyword-based image search engines. A fundamental issue underlying the success of existing image reranking approaches is the ability in identifying potentially useful recurrent patterns or relevant training examples from the initial search results. Ideally, these patterns and examples can be leveraged to upgrade the ranks of visually similar images, which are also likely to be relevant. The challenge, nevertheless, originates from the fact that keyword-based queries are used to be ambiguous, resulting in difficulty in predicting the search intention. Mining useful patterns and examples without understanding query is …
Tower Of Babel: A Crowdsourcing Game Building Sentiment Lexicons For Resource-Scarce Languages, Yoonsung Hong, Haewoon Kwak, Youngmin Baek, Sue. Moon
Tower Of Babel: A Crowdsourcing Game Building Sentiment Lexicons For Resource-Scarce Languages, Yoonsung Hong, Haewoon Kwak, Youngmin Baek, Sue. Moon
Research Collection School Of Computing and Information Systems
With the growing amount of textual data produced by online social media today, the demands for sentiment analysis are also rapidly increasing; and, this is true for worldwide. However, non-English languages often lack sentiment lexicons, a core resource in performing sentiment analysis. Our solution, Tower of Babel (ToB), is a language-independent sentiment-lexicon-generating crowdsourcing game. We conducted an experiment with 135 participants to explore the difference between our solution and a conventional manual annotation method. We evaluated ToB in terms of effectiveness, efficiency, and satisfactions. Based on the result of the evaluation, we conclude that sentiment classification via ToB is accurate, …
Predicting Sql Injection And Cross Site Scripting Vulnerabilities Through Mining Input Sanitization Patterns, Lwin Khin Shar, Hee Beng Kuan Tan
Predicting Sql Injection And Cross Site Scripting Vulnerabilities Through Mining Input Sanitization Patterns, Lwin Khin Shar, Hee Beng Kuan Tan
Research Collection School Of Computing and Information Systems
ContextSQL injection (SQLI) and cross site scripting (XSS) are the two most common and serious web application vulnerabilities for the past decade. To mitigate these two security threats, many vulnerability detection approaches based on static and dynamic taint analysis techniques have been proposed. Alternatively, there are also vulnerability prediction approaches based on machine learning techniques, which showed that static code attributes such as code complexity measures are cheap and useful predictors. However, current prediction approaches target general vulnerabilities. And most of these approaches locate vulnerable code only at software component or file levels. Some approaches also involve process attributes that …
Searching Visual Instances With Topology Checking And Context Modeling, Wei Zhang, Chong-Wah Ngo
Searching Visual Instances With Topology Checking And Context Modeling, Wei Zhang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Instance Search (INS) is a realistic problem initiated by TRECVID, which is to retrieve all occurrences of the querying object, location, or person from a large video collection. It is a fundamental problem with many applications, and also a challenging problem different from the traditional concept or near-duplicate (ND) search, since the relevancy is defined at instance level. True responses could exhibit various visual variations, such as being small on the image with different background, or showing a non-homography spatial configuration. Based on the Bag-of-Words model, we propose two techniques tailored for Instance Search. Specifically, we explore the use of …
Circular Reranking For Visual Search, Ting Yao, Chong-Wah Ngo
Circular Reranking For Visual Search, Ting Yao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Search reranking is regarded as a common way to boost retrieval precision. The problem nevertheless is not trivial especially when there are multiple features or modalities to be considered for search, which often happens in image and video retrieval. This paper proposes a new reranking algorithm, named circular reranking, that reinforces the mutual exchange of information across multiple modalities for improving search performance, following the philosophy that strong performing modality could learn from weaker ones, while weak modality does benefit from interacting with stronger ones. Technically, circular reranking conducts multiple runs of random walks through exchanging the ranking scores among …
Semi-Supervised Heterogeneous Fusion For Multimedia Data Co-Clustering, Lei Meng, Ah-Hwee Tan, Dong Xu
Semi-Supervised Heterogeneous Fusion For Multimedia Data Co-Clustering, Lei Meng, Ah-Hwee Tan, Dong Xu
Research Collection School Of Computing and Information Systems
Co-clustering is a commonly used technique for tapping the rich meta-information of multimedia web documents, including category, annotation, and description, for associative discovery. However, most co-clustering methods proposed for heterogeneous data do not consider the representation problem of short and noisy text and their performance is limited by the empirical weighting of the multi-modal features. In this paper, we propose a generalized form of Heterogeneous Fusion Adaptive Resonance Theory, called GHF-ART, for co-clustering of large-scale web multimedia documents. By extending the two-channel Heterogeneous Fusion ART (HF-ART) to multiple channels, GHF-ART is designed to handle multimedia data with an arbitrarily rich …