Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Evaluating bag-of-visual-words representations in scene classification
2007530 citationsYu–Gang Jiang, Chong‐Wah Ngo et al.profile →
Towards optimal bag-of-features for object categorization and semantic video retrieval
2007436 citationsYu–Gang Jiang, Chong‐Wah Ngo et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Chong‐Wah Ngo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chong‐Wah Ngo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chong‐Wah Ngo more than expected).
This network shows the impact of papers produced by Chong‐Wah Ngo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chong‐Wah Ngo. The network helps show where Chong‐Wah Ngo may publish in the future.
Co-authorship network of co-authors of Chong‐Wah Ngo
This figure shows the co-authorship network connecting the top 25 collaborators of Chong‐Wah Ngo.
A scholar is included among the top collaborators of Chong‐Wah Ngo based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Chong‐Wah Ngo. Chong‐Wah Ngo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhao, Wan‐Lei, Chong‐Wah Ngo, & Hanzi Wang. (2016). Fast Covariant VLAD for Image Search. IEEE Transactions on Multimedia. 18(9). 1843–1854.5 indexed citations
15.
Zhang, Hao, Lei Pang, Yijie Lu, & Chong‐Wah Ngo. (2016). VIREO @ TRECVID 2016: Multimedia Event Detection, Ad-hoc Video Search, Video-to-Text Description. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University).2 indexed citations
16.
Zhang, Hao, Yijie Lu, Maaike de Boer, et al.. (2015). VIREO-TNO @ TRECVID 2015: Multimedia Event Detection and Video Hyperlinking.. TRECVID.1 indexed citations
17.
Zhang, Wei, et al.. (2012). VIREO@TRECVID 2012: Searching with Topology, Recounting will Small Concepts, Learning with Free Examples. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University).4 indexed citations
18.
Ngo, Chong‐Wah, Yu–Gang Jiang, Xiao-Yong Wei, et al.. (2009). VIREO/DVMM at TRECVID 2009: High-Level Feature Extraction, Automatic Video Search, and Content-Based Copy Detection.. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University).26 indexed citations
19.
Ngo, Chong‐Wah, Yu–Gang Jiang, Xiao-Yong Wei, et al.. (2008). Beyond Semantic Search: What You Observe May Not Be What You Think.. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University).6 indexed citations
20.
Jiang, Yu–Gang, Xiao-Yong Wei, Chong‐Wah Ngo, et al.. (2006). Modeling Local Interest Points for Semantic Detection and Video Search at TRECVID 2006.. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University).3 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.