Zechao Li
ABSTRACT
The image-word correlation estimation is an essential issue in image annotation. In this paper, we propose a multi-correlation probabilistic matrix factorization (MPMF) algorithm for the correlation estimation. Different from the traditional solutions which treat the image-word correlation, image similarity and word relation independently or sequentially, in the proposed MPMF, these three elements are integrated together simultaneously and seamlessly. Specifically, we have derived two low-dimensional sets by conducting a joint factorization upon the word-to-image relation matrix, the image similarity matrix, and the word relation matrix to derive two low-dimensional sets of latent word factors and latent image factors. Finally, the annotation words of each untagged or noisily tagged image can be predicted by reconstructing the image-word correlations with the both derived latent factors. Experimental results on the Corel dataset and a Flickr image dataset show the superior performance of our proposed algorithm over the state-of-the-arts.
- P. Dugulu and K. Barnard. Object recognitions as machine translation: learning a lexicon for a fixed image vocabular. ECCV, 2002. Google Scholar
Digital Library
- S. Feng, R. Manmatha, and V. Lavrenko. Multiple bernoulli relevance models for image and video annotation. CVPR, pages 1002--1009, 2004. Google ScholarDigital Library
- J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. ACM SIGIR, pages 119--126, 2003. Google ScholarDigital Library
- R. Jin, J. Y. Chai, and L. Si. Effective automatic image annotation via a coherent language model and active learning. ACM SIGMM, pages 892--899, 2004. Google ScholarDigital Library
- Y. Jin, L. Khan, L. Wang, and M. Awad. Image annotation by combining multiple evidence & wordnet. ACM SIGMM, pages 706--715, 2005. Google ScholarDigital Library
- F. Kang, R. Jin, and R. Sukthankar. Correlated label propagation with application to multi-label learning. CVPR, pages 1719--1726, 2006. Google ScholarDigital Library
- V. Lavrenko, R. Manmatha, and J. Jeon. A model for learning the semantics of pictures. NIPS, 2004.Google Scholar
- J. Liu, M. Li, Q. Liu, H. Lu, and S. Ma. Image annotation via graph learning. PR, 42(2):218--228, 2009. Google ScholarDigital Library
- J. Liu, B. Wang, M. Li, M. Li, W. Ma, H. Lu, and S. Ma. Dual cross-media relevance model for image annotation. ACM SIGMM, pages 605--614, 2007. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. NIPS, 20:605--614, 2008.Google Scholar
- C. Wang, S. Yan, L. Zhang, and H. Zhang. Multi-label sparse coding for image annotation. CVPR, pages 1463--1650, 2009.Google ScholarCross Ref
Index Terms
- Image annotation using multi-correlation probabilistic matrix factorization
Recommendations
Image annotation via graph learning
Image annotation has been an active research topic in recent years due to its potential impact on both image understanding and web image search. In this paper, we propose a graph learning framework for image annotation. First, the image-based graph ...
Discovering phrase-level lexicon for image annotation
PCM'10: Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part IIn image annotation, the annotation words are expected to represent image content at both visual level and semantic level. However, a single word sometimes is ambiguous in annotation, for example, "apple" may refer to a fruit or a company. However, when ...
Probabilistic Matrix Factorization With Semantic And Visual Neighborhoods For Image Tag Completion
ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia RetrievalWe present an image tag completion method, namely PMF-SVN, where the key idea is to exploit images' Semantically and Visually similar Neighborhoods (SVNs) in the learning process of a Probabilistic Matrix Factorization (PMF) framework. We propose a two-...
Comments