C
Chong-Wah Ngo
Researcher at Singapore Management University
Publications - 292
Citations - 11602
Chong-Wah Ngo is an academic researcher from Singapore Management University. The author has contributed to research in topics: TRECVID & Computer science. The author has an hindex of 51, co-authored 275 publications receiving 10031 citations. Previous affiliations of Chong-Wah Ngo include Hong Kong University of Science and Technology & University of Hong Kong.
Papers
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Proceedings ArticleDOI
Evaluating bag-of-visual-words representations in scene classification
TL;DR: This study provides an empirical basis for designing visual-word representations that are likely to produce superior classification performance and applies techniques used in text categorization to generate image representations that differ in the dimension, selection, and weighting of visual words.
Proceedings ArticleDOI
Towards optimal bag-of-features for object categorization and semantic video retrieval
TL;DR: This paper evaluates various factors which govern the performance of Bag-of-features, and proposes a novel soft-weighting method to assess the significance of a visual word to an image and experimentally shows it can consistently offer better performance than other popular weighting methods.
Journal ArticleDOI
Video summarization and scene detection by graph modeling
TL;DR: In this application, video summaries that emphasize both content balance and perceptual quality can be generated directly from a temporal graph that embeds both the structure and attention information.
Proceedings ArticleDOI
Practical elimination of near-duplicates from web video search
TL;DR: The results of 24 queries in a data set of 12,790 videos retrieved from Google, Yahoo! and YouTube show that this hierarchical approach can dramatically reduce redundant video displayed to the user in the top result set, at relatively small computational cost.
Proceedings ArticleDOI
Deep-based Ingredient Recognition for Cooking Recipe Retrieval
Jingjing Chen,Chong-Wah Ngo +1 more
TL;DR: The feasibility of ingredient recognition is demonstrated and light is shed on this zero-shot problem peculiar to cooking recipe retrieval by experimenting on a large Chinese food dataset with images of highly complex dish appearance.