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Full-Text Articles in Entire DC Network
A Novel Algorithm Based On Visual Saliency Attention For Localization And Segmentation In Rapidly-Stained Leukocyte Images, Xin Zheng, Yong Wang, Guoyou Wang, Zhong Chen
A Novel Algorithm Based On Visual Saliency Attention For Localization And Segmentation In Rapidly-Stained Leukocyte Images, Xin Zheng, Yong Wang, Guoyou Wang, Zhong Chen
Research Collection School Of Computing and Information Systems
In this paper, we propose a fast hierarchical framework of leukocyte localization and segmentation in rapidly-stained leukocyte images (RSLI) with complex backgrounds and varying illumination. The proposed framework contains two main steps. First, a nucleus saliency model based on average absolute difference is built, which locates each leukocyte precisely while effectively removes dyeing impurities and erythrocyte fragments. Secondly, two different schemes are presented for segmenting the nuclei and cytoplasm respectively. As for nuclei segmentation, to solve the overlap problem between leukocytes, we extract the nucleus lobes first and further group them. The lobes extraction is realized by the histogram-based contrast …
Diamonds Are A Girl's Best Friend: Partial Order Reduction For Timed Automata With Abstractions, Henri Hansen, Shang-Wei Lin, Yang Liu, Truong Khanh Nguyen, Jun Sun
Diamonds Are A Girl's Best Friend: Partial Order Reduction For Timed Automata With Abstractions, Henri Hansen, Shang-Wei Lin, Yang Liu, Truong Khanh Nguyen, Jun Sun
Research Collection School Of Computing and Information Systems
A major obstacle for using partial order reduction in the context of real time verification is that the presence of clocks and clock constraints breaks the usual diamond structure of otherwise independent transitions. This is especially true when information of the relative values of clocks is preserved in the form of diagonal constraints. However, when diagonal constraints are relaxed by a suitable abstraction, some diamond structure is re-introduced in the zone graph. In this article, we introduce a variant of the stubborn set method for reducing an abstracted zone graph. Our method works with all abstractions, but especially targets situations …
Creating Autonomous Adaptive Agents In A Real-Time First-Person Shooter Computer Game, Di Wang, Ah-Hwee Tan
Creating Autonomous Adaptive Agents In A Real-Time First-Person Shooter Computer Game, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Games are good test-beds to evaluate AI methodologies. In recent years, there has been a vast amount of research dealing with real-time computer games other than the traditional board games or card games. This paper illustrates how we create agents by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first-person shooter computer game called Unreal Tournament. Rewards used for learning are either obtained from the game environment or estimated using the temporal difference learning scheme. In this way, the agents are able to acquire proper strategies and discover the effectiveness of different weapons without …
Influences Of Influential Users: An Empirical Study Of Music Social Network, Jing Ren, Zhiyong Cheng, Jialie Shen, Feida Zhu
Influences Of Influential Users: An Empirical Study Of Music Social Network, Jing Ren, Zhiyong Cheng, Jialie Shen, Feida Zhu
Research Collection School Of Computing and Information Systems
Influential user can play a crucial role in online social networks. This paper documents an empirical study aiming at exploring the effects of influential users in the context of music social network. To achieve this goal, music diffusion graph is developed to model how information propagates over network. We also propose a heuristic method to measure users' influences. Using the real data from Last. fm, our empirical test demonstrates key effects of influential users and reveals limitations of existing influence identification/characterization schemes.
A Simple Polynomial-Time Randomized Distributed Algorithm For Connected Row Convex Constraints, T. K. Satish Kumar, Nguyen Duc Thien, William Yeoh, Sven Koenig
A Simple Polynomial-Time Randomized Distributed Algorithm For Connected Row Convex Constraints, T. K. Satish Kumar, Nguyen Duc Thien, William Yeoh, Sven Koenig
Research Collection School Of Computing and Information Systems
In this paper, we describe a simple randomized algorithm that runs in polynomial time and solves connected row convex (CRC) constraints in distributed settings. CRC constraints generalize many known tractable classes of constraints like 2-SAT and implicational constraints. They can model problems in many domains including temporal reasoning and geometric reasoning, and generally speaking, play the role of "Gaussians" in the logical world. Our simple randomized algorithm for solving them in distributed settings, therefore, has a number of important applications. We support our claims through a theoretical analysis and empirical results.
Predicting The Popularity Of Web 2.0 Items Based On User Comments, Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, Kazunari Sugiyama
Predicting The Popularity Of Web 2.0 Items Based On User Comments, Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, Kazunari Sugiyama
Research Collection School Of Computing and Information Systems
In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible …
On Predicting Religion Labels In Microblogging Networks, Minh Thap Nguyen, Ee Peng Lim
On Predicting Religion Labels In Microblogging Networks, Minh Thap Nguyen, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Religious belief plays an important role in how people behave, influencing how they form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users' religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self and social variants for representing a user. We introduce the notion of representative user to …
Scalable Detection Of Missed Cross-Function Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Scalable Detection Of Missed Cross-Function Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Research Collection School Of Computing and Information Systems
Refactoring is an important way to improve the design of existing code. Identifying refactoring opportunities (i.e., code fragments that can be refactored) in large code bases is a challenging task. In this paper, we propose a novel, automated and scalable technique for identifying cross-function refactoring opportunities that span more than one function (e.g., Extract Method and Inline Method). The key of our technique is the design of efficient vector inlining operations that emulate the effect of method inlining among code fragments, so that the problem of identifying cross-function refactoring can be reduced to the problem of finding similar vectors before …
Learning Relative Similarity By Stochastic Dual Coordinate Ascent, Pengcheng Wu, Ding Yi, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi
Learning Relative Similarity By Stochastic Dual Coordinate Ascent, Pengcheng Wu, Ding Yi, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Learning relative similarity from pairwise instances is an important problem in machine learning and has a wide range of applications. Despite being studied for years, some existing methods solved by Stochastic Gradient Descent (SGD) techniques generally suffer from slow convergence. In this paper, we investigate the application of Stochastic Dual Coordinate Ascent (SDCA) technique to tackle the optimization task of relative similarity learning by extending from vector to matrix parameters. Theoretically, we prove the optimal linear convergence rate for the proposed SDCA algorithm, beating the well-known sublinear convergence rate by the previous best metric learning algorithms. Empirically, we conduct extensive …
Understanding The Paradigm Shift To Computational Social Science In The Presence Of Big Data, Ray M. Chang, Robert J. Kauffman, Young Ok Kwon
Understanding The Paradigm Shift To Computational Social Science In The Presence Of Big Data, Ray M. Chang, Robert J. Kauffman, Young Ok Kwon
Research Collection School Of Computing and Information Systems
The era of big data has created new opportunities for researchers to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of new data collection technologies, advanced data mining and analytics support, there seems to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts include social networks and blogs, political discourse, corporate announcements, digital journalism, mobile telephony, home entertainment, online gaming, financial services, online shopping, social advertising, and social commerce. The changing costs of data collection and …
Manifold Learning For Jointly Modeling Topic And Visualization, Tuan Minh Van Le, Hady W. Lauw
Manifold Learning For Jointly Modeling Topic And Visualization, Tuan Minh Van Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to …
Fsph: Fitted Spectral Hashing For Efficient Similarity Search, Yong-Dong Zhang, Yu Wang, Sheng Tang, Steven C. H. Hoi, Jin-Tao Li
Fsph: Fitted Spectral Hashing For Efficient Similarity Search, Yong-Dong Zhang, Yu Wang, Sheng Tang, Steven C. H. Hoi, Jin-Tao Li
Research Collection School Of Computing and Information Systems
Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, fitted spectral hashing (FSpH), to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among data items. We have found that this mapping on each PCA direction has certain regular pattern, and could be fitted well by S-curve function (Sigmoid function). …
Decentralized Stochastic Planning With Anonymity In Interactions, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Decentralized Stochastic Planning With Anonymity In Interactions, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Research Collection School Of Computing and Information Systems
In this paper, we solve cooperative decentralized stochastic planning problems, where the interactions between agents (specified using transition and reward functions) are dependent on the number of agents (and not on the identity of the individual agents) involved in the interaction. A collision of robots in a narrow corridor, defender teams coordinating patrol activities to secure a target, etc. are examples of such anonymous interactions. Formally, we consider problems that are a subset of the well known Decentralized MDP (DEC-MDP) model, where the anonymity in interactions is specified within the joint reward and transition functions. In this paper, not only …
Soml: Sparse Online Metric Learning With Application To Image Retrieval, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Ji Wan, Jintao Li
Soml: Sparse Online Metric Learning With Application To Image Retrieval, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Ji Wan, Jintao Li
Research Collection School Of Computing and Information Systems
Image similarity search plays a key role in many multimedia applications, where multimedia data (such as images and videos) are usually represented in high-dimensional feature space. In this paper, we propose a novel Sparse Online Metric Learning (SOML) scheme for learning sparse distance functions from large-scale high-dimensional data and explore its application to image retrieval. In contrast to many existing distance metric learning algorithms that are often designed for low-dimensional data, the proposed algorithms are able to learn sparse distance metrics from high-dimensional data in an efficient and scalable manner. Our experimental results show that the proposed method achieves better …
Mobile Humanoid Agent With Mood Awareness For Elderly Care, Di Wang, Ah-Hwee Tan
Mobile Humanoid Agent With Mood Awareness For Elderly Care, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Human, especially elderly, require frequent attention, continuous companionship, and deep understanding from the others. To provide more specific and appropriate tender care to the elderly, knowing their affective states is a great advantage. Recent work on human emotion recognition shows promising results that the expressive emotion can be successfully captured through visual, audio, and keyboard or touchpad stroke pattern signals. Furthermore, human activities are shown to be accurately recognizable with context by nonintrusive sensors within or connected to the smartphones. In this paper, we propose a computational model to characterize the affective states of the elderly based on the recognizable …
Cloud-Based Query Evaluation For Energy-Efficient Mobile Sensing, Tianli Mo, Sougata Sen, Lipyeow Lim, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Cloud-Based Query Evaluation For Energy-Efficient Mobile Sensing, Tianli Mo, Sougata Sen, Lipyeow Lim, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
In this paper, we reduce the energy overheads of continuous mobile sensing for context-aware applications that are interested in collective context or events. We propose a cloud-based query management and optimization framework, called CloQue, which can support concurrent queries, executing over thousands of individual smartphones. CloQue exploits correlation across context of different users to reduce energy overheads via two key innovations: i) Dynamically reordering the order of predicate processing to preferentially select predicates with not just lower sensing cost and higher selectivity, but that maximally reduce the uncertainty about other context predicates, and ii) intelligently propagating the query evaluation results …
Building Algorithm Portfolios For Memetic Algorithms, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau
Building Algorithm Portfolios For Memetic Algorithms, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
The present study introduces an automated mechanism to build algorithm portfolios for memetic algorithms. The objective is to determine an algorithm set involving combinations of crossover, mutation and local search operators based on their past performance. The past performance is used to cluster algorithm combinations. Top performing combinations are then considered as the members of the set. The set is expected to have algorithm combinations complementing each other with respect to their strengths in a portfolio setting. In other words, each algorithm combination should be good at solving a certain type of problem instances such that this set can be …
Decentralized Multi-Agent Reinforcement Learning In Average-Reward Dynamic Dcops, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein, Chongjie Zhang
Decentralized Multi-Agent Reinforcement Learning In Average-Reward Dynamic Dcops, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein, Chongjie Zhang
Research Collection School Of Computing and Information Systems
Researchers have introduced the Dynamic Distributed Constraint Optimization Problem (Dynamic DCOP) formulation to model dynamically changing multi-agent coordination problems, where a dynamic DCOP is a sequence of (static canonical) DCOPs, each partially different from the DCOP preceding it. Existing work typically assumes that the problem in each time step is decoupled from the problems in other time steps, which might not hold in some applications. Therefore, in this paper, we make the following contributions: (i) We introduce a new model, called Markovian Dynamic DCOPs (MD-DCOPs), where the DCOP in the next time step is a function of the value assignments …
Verifying Monadic Second-Order Properties Of Graph Programs, Christopher M. Poskitt, Detlef Plump
Verifying Monadic Second-Order Properties Of Graph Programs, Christopher M. Poskitt, Detlef Plump
Research Collection School Of Computing and Information Systems
The core challenge in a Hoare- or Dijkstra-style proof system for graph programs is in defining a weakest liberal precondition construction with respect to a rule and a postcondition. Previous work addressing this has focused on assertion languages for first-order properties, which are unable to express important global properties of graphs such as acyclicity, connectedness, or existence of paths. In this paper, we extend the nested graph conditions of Habel, Pennemann, and Rensink to make them equivalently expressive to monadic second-order logic on graphs. We present a weakest liberal precondition construction for these assertions, and demonstrate its use in verifying …
Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau
Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.
Facilitating Crowd Sourced Software Engineering Via Stack Overflow, Ohad Barzilay, Christoph Treude, Alexey Zagalsky
Facilitating Crowd Sourced Software Engineering Via Stack Overflow, Ohad Barzilay, Christoph Treude, Alexey Zagalsky
Research Collection School Of Computing and Information Systems
The open source community, as well as numerous technical blogs and community web sites, put online vast quantities of free source code, ranging from snippets to full-blown products. This code embodies the software development community’s domain knowledge, and mirrors the structure of the Internet: it is distributed rather than hierarchical; it is chaotic, incomplete, and inconsistent. StackOverflow.com is a Question and Answer (Q&A) website which uses social media to facilitate knowledge exchange between programmers by mitigating the pitfalls involved in using code from the Internet. Its design nurtures a community of developers, and enables crowd sourced software engineering activities ranging …
Click-Through-Based Cross-View Learning For Image Search, Yingwei Pan, Ting Yao, Tao Mei, Houqiang Li, Chong-Wah Ngo, Yong Rui
Click-Through-Based Cross-View Learning For Image Search, Yingwei Pan, Ting Yao, Tao Mei, Houqiang Li, Chong-Wah Ngo, Yong Rui
Research Collection School Of Computing and Information Systems
One of the fundamental problems in image search is to rank image documents according to a given textual query. Existing search engines highly depend on surrounding texts for ranking images, or leverage the query-image pairs annotated by human labelers to train a series of ranking functions. However, there are two major limitations: 1) the surrounding texts are often noisy or too few to accurately describe the image content, and 2) the human annotations are resourcefully expensive and thus cannot be scaled up. We demonstrate in this paper that the above two fundamental challenges can be mitigated by jointly exploring the …
User Daily Activity Pattern Learning: A Multi-Memory Modeling Approach, Shan Gao, Ah-Hwee Tan
User Daily Activity Pattern Learning: A Multi-Memory Modeling Approach, Shan Gao, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
In this paper, we propose a multi-memory model, ADLART model, to discover the daily activity pattern of a sensor monitored user from his/her activities of daily living (ADL). The proposed model mimics the human multiple memory system which comprises a working memory, an episodic memory, and a semantic memory. Through encoding user's daily activities patterns in episodic memory and extracting the regularities of activity routines in semantic memory, the ADLART system is able to learn, recognize, compare, and retrieve daily ADL patterns of the user. Experiments are presented to show the performance of the ADLART model using different parameter settings …
Fsph: Fitted Spectral Hashing For Efficient Similarity Search, Yong-Dong Zhang, Yu Wang, Sheng Tang, Steven C. H. Hoi, Jin-Tao Li
Fsph: Fitted Spectral Hashing For Efficient Similarity Search, Yong-Dong Zhang, Yu Wang, Sheng Tang, Steven C. H. Hoi, Jin-Tao Li
Research Collection School Of Computing and Information Systems
Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, fitted spectral hashing (FSpH), to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among data items. We have found that this mapping on each PCA direction has certain regular pattern, and could be fitted well by S-curve function (Sigmoid function). …
A Retail Bank's Bpm Experience, Shankararaman, Venky, Gottipati Swapna, Randall E. Duran
A Retail Bank's Bpm Experience, Shankararaman, Venky, Gottipati Swapna, Randall E. Duran
Research Collection School Of Computing and Information Systems
This real-life case study, which was undertaken by a leading financial services group in the Asia-Pacific region, is used to demonstrate the innovative use of BPM (Business Process Management) technology in a competitive business area. It describes how a BPM project, within the Application Verification and Capture (AVC), was conceived, designed and implemented in order to deliver strategic value to the organization. Hereafter, the financial services group will be referred to as “the bank”. The AVC project was targeted at one of the bank's processes called the Application Verification and Capture (AVC) process for unit trust products. This process involved …
Synchronicity: Pushing The Envelope Of Fine-Grained Localization With Distributed Mimo, Jie Xiong, Kyle Jamieson, Karthikeyan Sundaresan
Synchronicity: Pushing The Envelope Of Fine-Grained Localization With Distributed Mimo, Jie Xiong, Kyle Jamieson, Karthikeyan Sundaresan
Research Collection School Of Computing and Information Systems
Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and enough hardware infrastructure. Synchronicity is a location system that aims to push the envelope of highly-accurate localization systems further in both dimensions, requiring less line-of-sight and less infrastructure. With Distributed MIMO network of wireless LAN access points (APs) as a starting point, we leverage the time synchronization that such a network affords to localize with time-difference-of-arrival information at the APs. We contribute novel super-resolution signal processing algorithms and reflection path elimination schemes, yielding superior results even in …
Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition, Akshat Kumar, Sudhanshu Singh, Pranav Gupta, Gyana Parija
Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition, Akshat Kumar, Sudhanshu Singh, Pranav Gupta, Gyana Parija
Research Collection School Of Computing and Information Systems
We address the problem of minimizing staffing cost in a contact center subject to service level requirements over multiple weeks. We handle both the capacity planning and agent schedule generation aspect of this problem. Our work incorporates two unique business requirements. First, we develop techniques that can provide near-optimal staffing for 247 contact centers over long term, upto eight weeks, rather than planning myopically on a week-on-week basis. Second, our approach is usable in an online interactive setting in which staffing managers using our system expect high quality plans within a short time period. Results on large real world and …
Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe
Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe
Research Collection School Of Computing and Information Systems
To dissuade reckless driving and mitigate accidents, cities deploy resources to patrol roads. In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defenderattacker Stackelberg game. Previous work on Stackelberg security games has focused extensively on counterterrorism settings. STREETS moves beyond counterterrorism and represents the first use of Stackelberg games for traffic patrolling, in the process providing a novel algorithm for solving such games that addresses three major challenges in modeling and scale-up. First, there exists a high degree of unpredictability in travel times …
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee Peng Lim
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
As the rapid growth of online social media attracts a large number of Internet users, the large volume of content generated by these users also provides us with an opportunity to study the lexical variation of people of different ages. In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users’ ages. Our model inherently assumes that a topic has not only a word distribution but also an age distribution. We propose a Gibbs-EM algorithm to perform inference on our model. Empirical evaluation shows that our model can learn meaningful age-specific …
Placing Videos On A Semantic Hierarchy For Search Result Navigation, Song Tan, Yu-Gang Jiang, Chong-Wah Ngo
Placing Videos On A Semantic Hierarchy For Search Result Navigation, Song Tan, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Organizing video search results in a list view is widely adopted by current commercial search engines, which cannot support efficient browsing for complex search topics that have multiple semantic facets. In this article, we propose to organize video search results in a highly structured way. Specifically, videos are placed on a semantic hierarchy that accurately organizes various facets of a given search topic. To pick the most suitable videos for each node of the hierarchy, we define and utilize three important criteria: relevance, uniqueness, and diversity. Extensive evaluations on a large YouTube video dataset demonstrate the effectiveness of our approach.