Ye Liang
Papers
1
Total Citations
3
H-Index
1
About
Ye Liang is a computer vision researcher whose work centers on image representation, particularly the Bag-of-Features (BoF) model—a foundational approach for tasks like image classification, video search, and texture recognition. His most-cited study, "Study of BoF Model Based Image Representation" (2014, 3 citations), addresses a core challenge in the field: designing effective, orderless representations by quantizing local image descriptors. While his citation count remains modest, Liang’s contribution lies in clarifying and advancing the theoretical underpinnings of BoF, a model that has become a staple in computer vision pipelines. His work provides a systematic analysis of how BoF features can be optimized for robustness and scalability, offering practical insights for researchers building on this paradigm. Liang’s focus on fundamental representation issues highlights his commitment to solving the “first principles” of visual recognition, making his research a useful reference for students and practitioners seeking a deeper understanding of how machines interpret unordered visual data.
Research Focus
Key Achievements
Top Papers
- 1Study of BoF Model Based Image Representation3 citations · 2014