Liang-Tien Chia
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 2%
- Media Technology top 1%
- Computational Mechanics top 5%
- Information Systems top 2%
- Topics
- Advanced Image and Video Retrieval Techniques (51 papers)Image Retrieval and Classification Techniques (37 papers)Video Analysis and Summarization (30 papers)
In The Last Decade
Liang-Tien Chia
103 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 108
- Computer Vision and Pattern Recognition 1.7k
- Artificial Intelligence 511
- Media Technology 319
- Computational Mechanics 280
- Information Systems 262
Countries citing papers authored by Liang-Tien Chia
This map shows the geographic impact of Liang-Tien Chia'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 Liang-Tien Chia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liang-Tien Chia more than expected).
Fields of papers citing papers by Liang-Tien Chia
This network shows the impact of papers produced by Liang-Tien Chia. 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 Liang-Tien Chia. The network helps show where Liang-Tien Chia may publish in the future.
Co-authorship network of co-authors of Liang-Tien Chia
This figure shows the co-authorship network connecting the top 25 collaborators of Liang-Tien Chia. A scholar is included among the top collaborators of Liang-Tien Chia 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 Liang-Tien Chia. Liang-Tien Chia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 146 | |
| 2 | 6 | |
| 3 | 47 | |
| 4 | 2 | |
| 5 | Hierarchical word image representation for parts-based object recognition | 1 |
| 6 | 9 | |
| 7 | 22 | |
| 8 | 3 | |
| 9 | 5 | |
| 10 | 1 | |
| 11 | 3 | |
| 12 | 4 | |
| 13 | 22 | |
| 14 | 2 | |
| 15 | 4 | |
| 16 | 59 | |
| 17 | 18 | |
| 18 | UX- An Architecture Providing QoS-Aware and Federated Support for UDDI. | 56 |
| 19 | Image Retrieval Using Dominant Color Descriptor. | 3 |
| 20 | A web-based artistes management system: experiences and effects | 1 |
About Liang-Tien Chia
Liang-Tien Chia is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology, having authored 106 papers that have together received 2.3k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (51 papers), Image Retrieval and Classification Techniques (37 papers) and Video Analysis and Summarization (30 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.7k citations), Media Technology (319 citations) and Sensory Systems (129 citations). Liang-Tien Chia has collaborated with scholars based in Singapore, China and Australia. Frequent co-authors include Shenghua Gao, Ivor W. Tsang, Zhixiang Ren, Deepu Rajan, Bu‐Sung Lee, Peilin Zhao, Yiqun Hu, Chen Zhou, Min Xu and C. Zhou. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.
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.