1 research outputs found
Perceptual Visual Interactive Learning
Supervised learning methods are widely used in machine learning. However, the
lack of labels in existing data limits the application of these technologies.
Visual interactive learning (VIL) compared with computers can avoid semantic
gap, and solve the labeling problem of small label quantity (SLQ) samples in a
groundbreaking way. In order to fully understand the importance of VIL to the
interaction process, we re-summarize the interactive learning related
algorithms (e.g. clustering, classification, retrieval etc.) from the
perspective of VIL. Note that, perception and cognition are two main visual
processes of VIL. On this basis, we propose a perceptual visual interactive
learning (PVIL) framework, which adopts gestalt principle to design interaction
strategy and multi-dimensionality reduction (MDR) to optimize the process of
visualization. The advantage of PVIL framework is that it combines computer's
sensitivity of detailed features and human's overall understanding of global
tasks. Experimental results validate that the framework is superior to
traditional computer labeling methods (such as label propagation) in both
accuracy and efficiency, which achieves significant classification results on
dense distribution and sparse classes dataset