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Articles 151 - 180 of 183
Full-Text Articles in Data Storage Systems
Co-Reranking By Mutual Reinforcement For Image Search, Ting Yao, Tao Mei, Chong-Wah Ngo
Co-Reranking By Mutual Reinforcement For Image Search, Ting Yao, Tao Mei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Most existing reranking approaches to image search focus solely on mining “visual” cues within the initial search results. However, the visual information cannot always provide enough guidance to the reranking process. For example, different images with similar appearance may not always present the same relevant information to the query. Observing that multi-modality cues carry complementary relevant information, we propose the idea of co-reranking for image search, by jointly exploring the visual and textual information. Co-reranking couples two random walks, while reinforcing the mutual exchange and propagation of information relevancy across different modalities. The mutual reinforcement is iteratively updated to constrain …
Pagesense: Style-Wise Web Page Advertising, Lusong Li, Tao Mei, Xiang Niu, Chong-Wah Ngo
Pagesense: Style-Wise Web Page Advertising, Lusong Li, Tao Mei, Xiang Niu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper presents an innovative style-wise advertising platform for web page. Web page “style” mainly refers to visual effects, such as color and layout. Unlike the most popular ad-network such as Google AdSense which needs publishers to change the original structure of their pages and define the position and style of the embedded ads manually, stylewise page advertising aims to automatically deliver styleconsistent ads at proper positions within the web page, without breaking the layout of the original page. Our system is motivated from the fact that almost 90% web pages contain blank regions without any content. Given a web …
A Robust Damage Assessment Model For Corrupted Database Systems, Ge Fu, Hong Zhu, Yingjiu Li
A Robust Damage Assessment Model For Corrupted Database Systems, Ge Fu, Hong Zhu, Yingjiu Li
Research Collection School Of Computing and Information Systems
An intrusion tolerant database uses damage assessment techniques to detect damage propagation scales in a corrupted database system. Traditional damage assessment approaches in a intrusion tolerant database system can only locate damages which are caused by reading corrupted data. In fact, there are many other damage spreading patterns that have not been considered in traditional damage assessment model. In this paper, we systematically analyze inter-transaction dependency relationships that have been neglected in the previous research and propose four different dependency relationships between transactions which may cause damage propagation. We extend existing damage assessment model based on the four novel dependency …
Semantic Context Transfer Across Heterogeneous Sources For Domain Adaptive Video Search, Yu-Gang Jiang, Chong-Wah Ngo, Shih-Fu Chang
Semantic Context Transfer Across Heterogeneous Sources For Domain Adaptive Video Search, Yu-Gang Jiang, Chong-Wah Ngo, Shih-Fu Chang
Research Collection School Of Computing and Information Systems
Automatic video search based on semantic concept detectors has recently received significant attention. Since the number of available detectors is much smaller than the size of human vocabulary, one major challenge is to select appropriate detectors to response user queries. In this paper, we propose a novel approach that leverages heterogeneous knowledge sources for domain adaptive video search. First, instead of utilizing WordNet as most existing works, we exploit the context information associated with Flickr images to estimate query-detector similarity. The resulting measurement, named Flickr context similarity (FCS), reflects the co-occurrence statistics of words in image context rather than textual …
Semantics-Preserving Bag-Of-Words Models For Efficient Image Annotation, Lei Wu, Steven C. H. Hoi, Nenghai Yu
Semantics-Preserving Bag-Of-Words Models For Efficient Image Annotation, Lei Wu, Steven C. H. Hoi, Nenghai Yu
Research Collection School Of Computing and Information Systems
The Bag-of-Words (BoW) model is a promising image representation for annotation. One critical limitation of existing BoW models is the semantic loss during the codebook generation process, in which BoW simply clusters visual words in Euclidian space. However, distance between two visual words in Euclidean space does not necessarily reflect the semantic distance between the two concepts, due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a novel scheme for learning a codebook such that semantically related features will be mapped to the same visual word. In particular, we consider the distance between …
Scalable Detection Of Partial Near-Duplicate Videos By Visual-Temporal Consistency, Hung-Khoon Tan, Chong-Wah Ngo, Richang Hong, Tat-Seng Chua
Scalable Detection Of Partial Near-Duplicate Videos By Visual-Temporal Consistency, Hung-Khoon Tan, Chong-Wah Ngo, Richang Hong, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Following the exponential growth of social media, there now exist huge repositories of videos online. Among the huge volumes of videos, there exist large numbers of near-duplicate videos. Most existing techniques either focus on the fast retrieval of full copies or near-duplicates, or consider localization in a heuristic manner. This paper considers the scalable detection and localization of partial near-duplicate videos by jointly considering visual similarity and temporal consistency. Temporal constraints are embedded into a network structure as directed edges. Through the structure, partial alignment is novelly converted into a network flow problem where highly efficient solutions exist. To precisely …
Towards Google Challenge: Combining Contextual And Social Information For Web Video Categorization, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Towards Google Challenge: Combining Contextual And Social Information For Web Video Categorization, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Web video categorization is a fundamental task for web video search. In this paper, we explore the Google challenge from a new perspective by combing contextual and social information under the scenario of social web. The semantic meaning of text (title and tags), video relevance from related videos, and user interest induced from user videos, are integrated to robustly determine the video category. Experiments on YouTube videos demonstrate the effectiveness of the proposed solution. The performance reaches 60% improvement compared to the traditional text based classifiers.
Distribution-Based Concept Selection For Concept-Based Video Retrieval, Juan Cao, Hongfang Jing, Chong-Wah Ngo, Yongdong Zhang
Distribution-Based Concept Selection For Concept-Based Video Retrieval, Juan Cao, Hongfang Jing, Chong-Wah Ngo, Yongdong Zhang
Research Collection School Of Computing and Information Systems
Query-to-concept mapping plays one of the keys to concept-based video retrieval. Conventional approaches try to find concepts that are likely to co-occur in the relevant shots from the lexical or statistical aspects. However, the high probability of co-occurrence alone cannot ensure its effectiveness to distinguish the relevant shots from the irrelevant ones. In this paper, we propose distribution-based concept selection (DBCS) for query-to-concept mapping by analyzing concept score distributions of within and between relevant and irrelevant sets. In view of the imbalance between relevant and irrelevant examples, two variants of DBCS are proposed respectively by considering the two-sided and onesided …
Mining Globally Distributed Frequent Subgraphs In A Single Labeled Graph, Xing Jiang, Hui Xiong, Chen Wang, Ah-Hwee Tan
Mining Globally Distributed Frequent Subgraphs In A Single Labeled Graph, Xing Jiang, Hui Xiong, Chen Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Recent years have observed increasing efforts on graph mining and many algorithms have been developed for this purpose. However, most of the existing algorithms are designed for discovering frequent subgraphs in a set of labeled graphs only. Also, the few algorithms that find frequent subgraphs in a single labeled graph typically identify subgraphs appearing regionally in the input graph. In contrast, for real-world applications, it is commonly required that the identified frequent subgraphs in a single labeled graph should also be globally distributed. This paper thus fills this crucial void by proposing a new measure, termed G-Measure, to find globally …
Large-Scale Near-Duplicate Web Video Search: Challenge And Opportunity, Wan-Lei Zhao, Song Tan, Chong-Wah Ngo
Large-Scale Near-Duplicate Web Video Search: Challenge And Opportunity, Wan-Lei Zhao, Song Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
The massive amount of near-duplicate and duplicate web videos has presented both challenge and opportunity to multimedia computing. On one hand, browsing videos on Internet becomes highly inefficient for the need to repeatedly fast-forward videos of similar content. On the other hand, the tremendous amount of somewhat duplicate content also makes some traditionally difficult vision tasks become simple and easy. For example, annotating pictures can be as simple as recycling the tags of Internet images retrieved from image search engines. Such tasks, of either to eliminate or to recycle near-duplicates, can usually be achieved by the nearest neighbor search of …
Exploring Inter-Concept Relationship With Context Space For Semantic Video Indexing, Xiao-Yong Wei, Yu-Gang Jiang, Chong-Wah Ngo
Exploring Inter-Concept Relationship With Context Space For Semantic Video Indexing, Xiao-Yong Wei, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Semantic concept detectors are often individually and independently developed. Using peripherally related concepts for leveraging the power of joint detection, which is referred to as context-based concept fusion (CBCF), has been one of the focus studies in recent years. This paper proposes the construction of a context space and the exploration of the space for CBCF. Context space considers the global consistency of concept relationship, addresses the problem of missing annotation, and is extensible for cross-domain contextual fusion. The space is linear and can be built by modeling the inter-concept relationship through annotation provided by either manual labeling or machine …
Visual Word Proximity And Linguistics For Semantic Video Indexing And Near-Duplicate Retrieval, Yu-Gang Jiang, Chong-Wah Ngo
Visual Word Proximity And Linguistics For Semantic Video Indexing And Near-Duplicate Retrieval, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Bag-of-visual-words (BoW) has recently become a popular representation to describe video and image content. Most existing approaches, nevertheless, neglect inter-word relatedness and measure similarity by bin-to-bin comparison of visual words in histograms. In this paper, we explore the linguistic and ontological aspects of visual words for video analysis. Two approaches, soft-weighting and constraint-based earth mover’s distance (CEMD), are proposed to model different aspects of visual word linguistics and proximity. In soft-weighting, visual words are cleverly weighted such that the linguistic meaning of words is taken into account for bin-to-bin histogram comparison. In CEMD, a cross-bin matching algorithm is formulated such …
Fusing Semantics, Observability, Reliability And Diversity Of Concept Detectors For Video Search, Xiao-Yong Wei, Chong-Wah Ngo
Fusing Semantics, Observability, Reliability And Diversity Of Concept Detectors For Video Search, Xiao-Yong Wei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Effective utilization of semantic concept detectors for large-scale video search has recently become a topic of intensive studies. One of main challenges is the selection and fusion of appropriate detectors, which considers not only semantics but also the reliability of detectors, observability and diversity of detectors in target video domains. In this paper, we present a novel fusion technique which considers different aspects of detectors for query answering. In addition to utilizing detectors for bridging the semantic gap of user queries and multimedia data, we also address the issue of "observability gap" among detectors which could not be directly inferred …
Modeling Video Hyperlinks With Hypergraph For Web Video Reranking, Hung-Khoon Tan, Chong-Wah Ngo, Xiao Wu
Modeling Video Hyperlinks With Hypergraph For Web Video Reranking, Hung-Khoon Tan, Chong-Wah Ngo, Xiao Wu
Research Collection School Of Computing and Information Systems
In this paper, we investigate a novel approach of exploiting visual-duplicates for web video reranking using hypergraph. Current graph-based reranking approaches consider mainly the pair-wise linking of keyframes and ignore reliability issues that are inherent in such representation. We exploit higher order relation to overcome the issues of missing links in visual-duplicate keyframes and in addition identify the latent relationships among keyframes. Based on hypergraph, we consider two groups of video threads: visual near-duplicate threads and story threads, to hyperlink web videos and describe the higher order information existing in video content. To facilitate reranking using random walk algorithm, the …
Ontology-Based Visual Word Matching For Near-Duplicate Retrieval, Yu-Gang Jiang, Chong-Wah Ngo
Ontology-Based Visual Word Matching For Near-Duplicate Retrieval, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper proposes a novel approach to exploit the ontological relationship of visual words by linguistic reasoning. A visual word ontology is constructed to facilitate the rigorous evaluation of linguistic similarity across visual words. The linguistic similarity measurement enables cross-bin matching of visual words, compromising the effectiveness and speed of conventional keypoint matching and bag-of-word approaches. A constraint EMD is proposed and experimented to efficiently match visual words. Empirical findings indicate that the proposed approach offers satisfactory performance to near-duplicate retrieval, while still enjoying the merit of speed efficiency compared with other techniques.
Bag-Of-Visual-Words Expansion Using Visual Relatedness For Video Indexing, Yu-Gang Jiang, Chong-Wah Ngo
Bag-Of-Visual-Words Expansion Using Visual Relatedness For Video Indexing, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Bag-of-visual-words (BoW) has been popular for visual classification in recent years. In this paper, we propose a novel BoW expansion method to alleviate the effect of visual word correlation problem. We achieve this by diffusing the weights of visual words in BoW based on visual word relatedness, which is rigorously defined within a visual ontology. The proposed method is tested in video indexing experiment on TRECVID-2006 video retrieval benchmark, and an improvement of 7% over the traditional BoW is reported.
Multimodal News Story Clustering With Pairwise Visual Near-Duplicate Constraint, Xiao Wu, Chong-Wah Ngo, Alexander G. Hauptmann
Multimodal News Story Clustering With Pairwise Visual Near-Duplicate Constraint, Xiao Wu, Chong-Wah Ngo, Alexander G. Hauptmann
Research Collection School Of Computing and Information Systems
Story clustering is a critical step for news retrieval, topic mining, and summarization. Nonetheless, the task remains highly challenging owing to the fact that news topics exhibit clusters of varying densities, shapes, and sizes. Traditional algorithms are found to be ineffective in mining these types of clusters. This paper offers a new perspective by exploring the pairwise visual cues deriving from near-duplicate keyframes (NDK) for constraint-based clustering. We propose a constraint-driven co-clustering algorithm (CCC), which utilizes the near-duplicate constraints built on top of text, to mine topic-related stories and the outliers. With CCC, the duality between stories and their underlying …
Concept Detection: Convergence To Local Features And Opportunities Beyond, Shih-Fu Chang, Junfeng He, Yu-Gang Jiang, Elie El Khoury, Chong-Wah Ngo, Akira Yanagawa, Eric Zavesky
Concept Detection: Convergence To Local Features And Opportunities Beyond, Shih-Fu Chang, Junfeng He, Yu-Gang Jiang, Elie El Khoury, Chong-Wah Ngo, Akira Yanagawa, Eric Zavesky
Research Collection School Of Computing and Information Systems
No abstract provided.
Columbia University/Vireo-Cityu/Irit Trecvid2008 High-Level Feature Extraction And Interactive Video Search, Shih-Fu Chang, Junfeng He, Yu-Gang Jiang, Elie El Khoury, Chong-Wah Ngo, Akira Yanagawa, Eric Zavesky
Columbia University/Vireo-Cityu/Irit Trecvid2008 High-Level Feature Extraction And Interactive Video Search, Shih-Fu Chang, Junfeng He, Yu-Gang Jiang, Elie El Khoury, Chong-Wah Ngo, Akira Yanagawa, Eric Zavesky
Research Collection School Of Computing and Information Systems
In this report, we present overview and comparative analysis of our HLF detection system, which achieves the top performance among all type-A submissions in 2008. We also describe preliminary evaluation of our video search system, CuZero, in the interactive search task.
Ontology-Enriched Semantic Space For Video Search, Xiao-Yong Wei, Chong-Wah Ngo
Ontology-Enriched Semantic Space For Video Search, Xiao-Yong Wei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Multimedia-based ontology construction and reasoning have recently been recognized as two important issues in video search, particularly for bridging semantic gap. The lack of coincidence between low-level features and user expectation makes concept-based ontology reasoning an attractive midlevel framework for interpreting high-level semantics. In this paper, we propose a novel model, namely ontology-enriched semantic space (OSS), to provide a computable platform for modeling and reasoning concepts in a linear space. OSS enlightens the possibility of answering conceptual questions such as a high coverage of semantic space with minimal set of concepts, and the set of concepts to be developed for …
Evaluating Bag-Of-Visual-Words Representations In Scene Classification, Jun Yang, Yu-Gang Jiang, Alexander G. Hauptmann, Chong-Wah Ngo
Evaluating Bag-Of-Visual-Words Representations In Scene Classification, Jun Yang, Yu-Gang Jiang, Alexander G. Hauptmann, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Based on keypoints extracted as salient image patches, an image can be described as a “bag of visual words” and this representation has been used in scene classification. The choice of dimension, selection, and weighting of visual words in this representation is crucial to the classification performance but has not been thoroughly studied in previous work. Given the analogy between this representation and the bag-of-words representation of text documents, we apply techniques used in text categorization, including term weighting, stop word removal, feature selection, to generate image representations that differ in the dimension, selection, and weighting of visual words. The …
Practical Elimination Of Near-Duplicates From Web Video Search, Xiao Wu, Alexander G. Hauptmann, Chong-Wah Ngo
Practical Elimination Of Near-Duplicates From Web Video Search, Xiao Wu, Alexander G. Hauptmann, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Current web video search results rely exclusively on text keywords or user-supplied tags. A search on typical popular video often returns many duplicate and near-duplicate videos in the top results. This paper outlines ways to cluster and filter out the nearduplicate video using a hierarchical approach. Initial triage is performed using fast signatures derived from color histograms. Only when a video cannot be clearly classified as novel or nearduplicate using global signatures, we apply a more expensive local feature based near-duplicate detection which provides very accurate duplicate analysis through more costly computation. The results of 24 queries in a data …
Towards Optimal Bag-Of-Features For Object Categorization And Semantic Video Retrieval, Yu-Gang Jiang, Chong-Wah Ngo, Jun Yang
Towards Optimal Bag-Of-Features For Object Categorization And Semantic Video Retrieval, Yu-Gang Jiang, Chong-Wah Ngo, Jun Yang
Research Collection School Of Computing and Information Systems
Bag-of-features (BoF) deriving from local keypoints has recently appeared promising for object and scene classification. Whether BoF can naturally survive the challenges such as reliability and scalability of visual classification, nevertheless, remains uncertain due to various implementation choices. In this paper, we evaluate various factors which govern the performance of BoF. The factors include the choices of detector, kernel, vocabulary size and weighting scheme. We offer some practical insights in how to optimize the performance by choosing good keypoint detector and kernel. For the weighting scheme, we propose a novel soft-weighting method to assess the significance of a visual word …
Modeling Local Interest Points For Semantic Detection And Video Search At Trecvid 2006, Yu-Gang Jiang, Xiaoyong Wei, Chong-Wah Ngo, Hung-Khoon Tan, Wanlei Zhao, Xiao Wu
Modeling Local Interest Points For Semantic Detection And Video Search At Trecvid 2006, Yu-Gang Jiang, Xiaoyong Wei, Chong-Wah Ngo, Hung-Khoon Tan, Wanlei Zhao, Xiao Wu
Research Collection School Of Computing and Information Systems
Local interest points (LIPs) and their features have been shown to obtain surprisingly good results in object detection and recognition. Its effectiveness and scalability, however, have not been seriously addressed in large-scale multimedia database, for instance TRECVID benchmark. The goal of our works is to investigate the role and performance of LIPs, when coupling with multi-modality features, for high-level feature extraction and automatic video search.
Splash: Systematic Proteomics Laboratory Analysis And Storage Hub, Siaw Ling Lo, You Tao, Qingsong Lin, Shashikant B. Joshi, Maxey Chung, Choy Leong Hew
Splash: Systematic Proteomics Laboratory Analysis And Storage Hub, Siaw Ling Lo, You Tao, Qingsong Lin, Shashikant B. Joshi, Maxey Chung, Choy Leong Hew
Research Collection School Of Computing and Information Systems
In the field of proteomics, the increasing difficulty to unify the data format, due to the different platforms/instrumentation and laboratory documentation systems, greatly hinders experimental data verification, exchange, and comparison. Therefore, it is essential to establish standard formats for every necessary aspect of proteomics data. One of the recently published data models is the proteomics experiment data repository [Taylor, C. F., Paton, N. W., Garwood, K. L., Kirby, P. D. et al., Nat. Biotechnol. 2003, 21, 247-254]. Compliant with this format, we developed the systematic proteomics laboratory analysis and storage hub (SPLASH) database system as an informatics infrastructure to support …
Threading And Autodocumenting News Videos: A Promising Solution To Rapidly Browse News Topics, Xiao Wu, Chong-Wah Ngo, Qing Li
Threading And Autodocumenting News Videos: A Promising Solution To Rapidly Browse News Topics, Xiao Wu, Chong-Wah Ngo, Qing Li
Research Collection School Of Computing and Information Systems
This paper describes the techniques in threading and autodocumenting news stories according to topic themes. Initially, we perform story clustering by exploiting the duality between stories and textual-visual concepts through a co-clustering algorithm. The dependency among stories of a topic is tracked by exploring the textual-visual novelty and redundancy of stories. A novel topic structure that chains the dependencies of stories is then presented to facilitate the fast navigation of the news topic. By pruning the peripheral and redundant news stories in the topic structure, a main thread is extracted for autodocumentary
Effect Of Training Datasets On Support Vector Machine Prediction Of Protein-Protein Interactions, Siaw Ling Lo, Cong Zhong Cai, Maxey Chung, Yu Zong Chen
Effect Of Training Datasets On Support Vector Machine Prediction Of Protein-Protein Interactions, Siaw Ling Lo, Cong Zhong Cai, Maxey Chung, Yu Zong Chen
Research Collection School Of Computing and Information Systems
Knowledge of protein-protein interaction is useful for elucidating protein function via the concept of 'guilt-by-association'. A statistical learning method, Support Vector Machine (SVM), has recently been explored for the prediction of protein-protein interactions using artificial shuffled sequences as hypothetical noninteracting proteins and it has shown promising results (Bock, J. R., Gough, D. A., Bioinformatics 2001, 17, 455-460). It remains unclear however, how the prediction accuracy is affected if real protein sequences are used to represent noninteracting proteins. In this work, this effect is assessed by comparison of the results derived from the use of real protein sequences with that derived …
Guest Editorial: Text And Web Mining, Ah-Hwee Tan, Philip S. Yu
Guest Editorial: Text And Web Mining, Ah-Hwee Tan, Philip S. Yu
Research Collection School Of Computing and Information Systems
Text mining and web mining are two interrelated fields that have received a lot of attention in recent years. Text mining [1, 2] is concerned with the analysis of very large document collections and the extraction of hidden knowledge from text-based data. Web mining [3] refers to the analysis and mining of all web-related data, including web content, hyperlink structure, and web access statistics.
Motion Retrieval By Temporal Slices Analysis, Chong-Wah Ngo, Chong-Wah Ngo, Hong-Jiang Zhang
Motion Retrieval By Temporal Slices Analysis, Chong-Wah Ngo, Chong-Wah Ngo, Hong-Jiang Zhang
Research Collection School Of Computing and Information Systems
In this papel; we investigate video shots retrieval based on the analysis of temporal slice images. Temporal slices are a set of2D images extracted along the time dimension of image sequences. They encode rich set of motion clues for shot similarity measure. Because motion is depicted as texture orientation in temporal slices, we utilize various texture features such as tensor histogram, Gabor feature, and the statistical feature of co-occurrence matrix extracted directly from slices for motion description and retrieval. In this way, motion retrieval can be treated in a similar way as texture retrieval problem. Experimental results indicate that the …
On Clustering And Retrieval Of Video Shots Through Temporal Slices Analysis, Chong-Wah Ngo, Ting-Chuen Pong, Hong-Jiang Zhang
On Clustering And Retrieval Of Video Shots Through Temporal Slices Analysis, Chong-Wah Ngo, Ting-Chuen Pong, Hong-Jiang Zhang
Research Collection School Of Computing and Information Systems
Based on the analysis of temporal slices, we propose novel approaches for clustering and retrieval of video shots. Temporal slices are a set of two-dimensional (2-D) images extracted along the time dimension of an image volume. They encode rich set of visual patterns for similarity measure. In this paper, we first demonstrate that tensor histogram features extracted from temporal slices are suitable for motion retrieval. Subsequently, we integrate both tensor and color histograms for constructing a two-level hierarchical clustering structure. Each cluster in the top level contains shots with similar color while each cluster in bottom level consists of shots …