- Research Article
- Open Access
Manifold-Ranking-Based Keyword Propagation for Image Retrieval
EURASIP Journal on Advances in Signal Processingvolume 2006, Article number: 079412 (2006)
A novel keyword propagation method is proposed for image retrieval based on a recently developed manifold-ranking algorithm. In contrast to existing methods which train a binary classifier for each keyword, our keyword model is constructed in a straightforward manner by exploring the relationship among all images in the feature space in the learning stage. In relevance feedback, the feedback information can be naturally incorporated to refine the retrieval result by additional propagation processes. In order to speed up the convergence of the query concept, we adopt two active learning schemes to select images during relevance feedback. Furthermore, by means of keyword model update, the system can be self-improved constantly. The updating procedure can be performed online during relevance feedback without extra offline training. Systematic experiments on a general-purpose image database consisting of 5 000 Corel images validate the effectiveness of the proposed method.
Chang S-K, Hsu A: Image information systems: where do we go from here? IEEE Transactions on Knowledge and Data Engineering 1992, 4(5):431–442. 10.1109/69.166986
Tamura H, Yokoya N: Image database systems: a survey. Pattern Recognition 1984, 17(1):29–43. 10.1016/0031-3203(84)90033-5
Shen HT, Ooi BC, Tan K-L: Giving meanings to WWW images. Proceedings of 8th ACM International Conference on Multimedia, October–November 2000, Los Angeles, Calif, USA 39–47.
He J, Li M, Zhang H-J, Tong H, Zhang C: Manifold-ranking based image retrieval. Proceedings of 12th Annual ACM International Conference on Multimedia, October 2004, New York, NY, USA 9–16.
Huang J, Kumar SR, Mitra M, Zhu W-J, Zabih R: Image indexing using color correlograms. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '97), June 1997, San Juan, Puerto Rico, USA 762–768.
Pass G, Zabih R, Miller J: Comparing images using color coherence vectors. Proceedings of 4th ACM International Conference on Multimedia, November 1996, Boston, Mass, USA 65–73.
Swain MJ, Ballard DH: Color indexing. International Journal of Computer Vision 1991, 7(1):11–32. 10.1007/BF00130487
Liu F, Picard RW: Periodicity, directionality, and randomness: wold features for image modeling and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996, 18(7):722–733. 10.1109/34.506794
Manjunath BS, Ma W-Y: Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996, 18(8):837–842. 10.1109/34.531803
Ze Wang J, Wiederhold G, Firschein O, Wei SX: Content-based image indexing and searching using Daubechies' wavelets. International Journal of Digital Libraries 1998, 1(4):311–328. 10.1007/s007990050026
Schmid C, Mohr R: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(5):530–535. 10.1109/34.589215
Zhou XS, Rui Y, Huang TS: Water-filling: a novel way for image structural feature extraction. Proceedings of IEEE International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 2: 570–574.
Chang E, Goh K, Sychay G, Wu G: CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology 2003, 13(1):26–38. 10.1109/TCSVT.2002.808079
Jing F, Li M, Zhang H-J, Zhang B: Keyword propagation for image retrieval. Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS '04), May 2004, Vancouver, British Columbia, Canada 2: 53–56.
Lu Y, Hu C, Zhu X, Zhang H-J, Yang Q: A unified framework for semantics and feature based relevance feedback in image retrieval systems. Proceedings of 8th ACM International Conference on Multimedia, October–November 2000, Los Angeles, Calif, USA 31–37.
Zhang L, Lin F, Zhang B: Support vector machine learning for image retrieval. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 2: 721–724.
Tong S, Chang E: Support vector machine active learning for image retrieval. Proceedings of 9th ACM International Conference on Multimedia, September–October 2001, Ottawa, Ontario, Canada 9: 107–118.
Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B: Learning with local and global consistency. Proceedings of 17th Annual Conference on Neural Information Processing Systems (NIPS '03), December 2003, Vancouver, British Columbia, Canada
Zhou D, Weston J, Gretton A, Bousquet O, Schölkopf B: Ranking on data manifolds. Proceedings of 17th Annual Conference on Neural Information Processing Systems (NIPS '03), December 2003, Vancouver, British Columbia, Canada
Li B, Chang E, Li C-S: Learning image query concepts via intelligent sampling. Proceedings of IEEE International Conference on Multimedia and Expo (ICME '01), August 2001, Tokyo, Japan 961–964.
Jing F, Li M, Zhang H-J, Zhang B: An effective region-based image retrieval framework. Proceedings of 10th ACM International Conference on Multimedia, December 2002, Juan-les-Pins, France 456–465.