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Articles 121 - 124 of 124
Full-Text Articles in Data Storage Systems
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 …
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 …
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 …
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.