Abstract
High quality ground truth data is essential for the development of image recognition systems. General purpose datasets are widely used in research, but they are not suitable as training sets for specialized real-world recognition tasks. The manual annotation of custom ground truth data sets is expensive, but machine learning techniques can be applied to preprocess image data and facilitate annotation. We propose a semi-automatic image annotation process, which clusters images according to similarity in a bag-of-features (BoF) approach. Clusters of images can be efficiently annotated in one go. The system recalculates the clustering continuously, based on partial annotations provided during annotation, by weighting BoF vector elements to increase intra-cluster similarity. Visualization of top-weighted codebook elements allows users to estimate the quality of annotations and of the recalculated clustering.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Moehrmann, J., Heidemann, G.: Efficient development of user-defined image recognition systems. In: Park, J.-I., Kim, J. (eds.) ACCV Workshops 2012, Part I. LNCS, vol. 7728, pp. 242–253. Springer, Heidelberg (2013)
Moehrmann, J., Heidemann, G.: Efficient Annotation of Image Data Sets for Computer Vision Applications. In: International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications, pp. 2:1–2:6 (2012)
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision 77(1), 157–173 (2008)
von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: ACM CHI, pp. 319–326 (2004)
Šimko, J., Bieliková, M.: Personal image tagging: a game-based approach. In: Proceedings of the Intl. Conference on Semantic Systems, pp. 88–93. ACM (2012)
Kumar, N., Kummamuru, K.: Semi-supervised clustering with metric learning using relative comparisons. IEEE Transactions on Knowledge and Data Engineering 20(4), 496–503 (2008)
Cai, H., Yan, F., Mikolajczyk, K.: Learning weights for codebook in image classification and retrieval. In: IEEE CVPR, pp. 2320–2327 (2010)
Xing, E., Ng, A., Jordan, M., Russell, S.: Distance metric learning, with application to clustering with side-information. Advances in Neural Information Processing Systems 15, 505–512 (2002)
Lee, D., Seung, H.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Chen, Y., Rege, M., Dong, M., Hua, J.: Non-negative matrix factorization for semi-supervised data clustering. Knowledge and Information Systems 17(3), 355–379 (2008)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Manjunath, B.S., Ohm, J.-R., Vasudevan, V.V., Yamada, A.: Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 703–715 (2001)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Jiang, Y.-G., Yang, J., Ngo, C.-W., Hauptmann, A.G.: Representations of keypoint-based semantic concept detection: A comprehensive study. IEEE Transactions on Multimedia 12(1), 42–53 (2010)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE CVPR, vol. 2, pp. 2169–2178 (2006)
Fruchterman, T., Reingold, E.: Graph drawing by force-directed placement. Software: Practice and Experience 21(11), 1129–1164 (1991)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Workshop on Generative-Model Based Vision (2004)
Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(3), 416–431 (2006b)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moehrmann, J., Heidemann, G. (2013). Semi-automatic Image Annotation. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-40246-3_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40245-6
Online ISBN: 978-3-642-40246-3
eBook Packages: Computer ScienceComputer Science (R0)