Contemporary visual learning methods for visual content mining tasks are plagued by several critical and fundamental challenges: (1) the unavailability of large annotated datasets prevents effective supervised learning; (2) the variability in different working environments challenges the generalization of inductive learning approaches; and (3) the high-dimensionality of these tasks confronts the efficiency of many existing learning techniques. The goal of this research project is to overcome these challenges by exploring a novel transductive learning approach.
The approach provides a unified framework accommodating four subtasks: (1) transduction that integrates unlabelled and labeled data to alleviate the challenge of limited supervision and to enable automatic annotation propagation; (2) model transduction that automatically adapts a learned model to untrained environments for efficient model reuse; (3) co-transduction that facilitates transduction with multi-modalities to handle high-dimensionality in visual data; and (4) co-inference that exploits the interactions among multiple modalities to enable efficient model transduction.
The research is linked to educational activities including the development of an integrated course of content-based visual data mining and the development of innovative course projects to engage students in research. The project disseminates research to other research communities through organizing workshops and tutorials, and to the general public, minority groups and woman students through creating Open House events.
The results of this project will lead to significant improvement on the quality of content-based and object-level multimedia retrieval, will greatly benefit visual recognition that requires large datasets for training and evaluation, will significantly reduce the efforts of training brand new models for un-trained scenarios, and will be very useful in intelligent video surveillance applications thus having a great impact on homeland security. A website, www.ece.nwu.edu/~yingwu, contains research results, including demos, constructed benchmark datasets and software can be accessed.
