Large-Scale Image Annotation by Efficient and Robust Kernel Metric Learning

• Published in: 2013 IEEE International Conference on Computer Vision pp. 1609 - 1616
• Main Authors: Zheyun Feng, Rong Jin, Jain, Anil
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.12.2013
• Subjects: Algorithm design and analysis; Algorithms; Annotations; Approximation algorithms; Approximation methods; Computational efficiency; Computer vision; Conferences; Efficient; Image Annotation; Kernel; Kernel Metric Learning; Kernels; Learning; Measurement; Regression; Semantics; Theoretical guarantee; Training
• ISSN: 1550-5499
• DOI: 10.1109/ICCV.2013.203

One of the key challenges in search-based image annotation models is to define an appropriate similarity measure between images. Many kernel distance metric learning (KML) algorithms have been developed in order to capture the nonlinear relationships between visual features and semantics of the images. One fundamental limitation in applying KML to image annotation is that it requires converting image annotations into binary constraints, leading to a significant information loss. In addition, most KML algorithms suffer from high computational cost due to the requirement that the learned matrix has to be positive semi-definitive (PSD). In this paper, we propose a robust kernel metric learning (RKML) algorithm based on the regression technique that is able to directly utilize image annotations. The proposed method is also computationally more efficient because PSD property is automatically ensured by regression. We provide the theoretical guarantee for the proposed algorithm, and verify its efficiency and effectiveness for image annotation by comparing it to state-of-the-art approaches for both distance metric learning and image annotation.