Abstract
In this paper, we address a specific image classification task, i.e. to group images according to whether they were taken by photographers or home users. Firstly, a set of low-level features explicitly related to such high-level semantic concept are investigated together with a set of general-purpose low-level features. Next, two different schemes are proposed to find out those most discriminative features and feed them to suitable classifiers: one resorts to boosting to perform feature selection and classifier training simultaneously; the other makes use of the information of the label by Principle Component Analysis for feature re-extraction and feature de-correlation; followed by Maximum Marginal Diversity for feature selection and Bayesian classifier or Support Vector Machine for classification. In addition, we show an application in No-Reference holistic quality assessment as a natural extension of such image classification. Experimental results demonstrate the effectiveness of our methods.
This work was performed at Microsoft Research Asia.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Athitsos, V., et al.: Distinguishing photographs and graphics on the World Wide Web. In: IEEE Workshop on CBAIVL (1997)
Chang, T., et al.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. on Image Processing 2, 429–441 (1993)
Friedman, J., et al.: Additive logistic regression: a statistical view of boosting. The Annual of Statistics 28(2), 337–374 (2000)
Hasler, D., et al.: Measuring colorfulness in real images. In: SPIE, vol. 5007, pp. 87–95 (2003)
Hastie, T., et al.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
He, J.R., et al.: W-Boost and its application to web image classification. In: Proc. ICPR (2004)
Huang, J., et al.: Image indexing using color correlogram. In: Proc. CVPR, pp. 762–768 (1997)
Ma, Y.F., et al.: A user attention model for video summarization. In: ACM Multimedia, pp. 533–542 (2002)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. on PAMI 11, 674–693 (1989)
Mao, J., et al.: Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition 25, 173–188 (1992)
Oliveira, C.J.S., et al.: Classifying images collected on the World Wide Web. In: SIGGRAPH, pp. 327–334 (2002)
Pass, G.: Comparing images using color coherence vectors. In: ACM Multimedia, pp. 65–73 (1997)
Serrano, N., et al.: A computational efficient approach to indoor/outdoor scene classification. In: Proc. ICPR, pp. 146–149 (2002)
Sheikh, H.R., et al.: Blind quality assessment for JPEG2000 compressed images. In: ICSSC (2002)
Stricker, M., et al.: Similarity of color images. In: SPIE, vol. 2420, pp. 381–392 (1995)
Swain, M., et al.: Color indexing. Int. Journal of Computer Vision 7(1), 11–32 (1991)
Tamura, H., et al.: Texture features corresponding to visual perception. IEEE Trans. on SMC 8, 460–473 (1978)
Tong, H.H., et al.: No-reference quality assessment for JPEG2000 compressed images. In: Proc. ICIP (2004)
Tong, H.H., et al.: Blur detection for digital images using wavelet transform. In: Proc. ICME (2004)
Vasconcelos, N., et al.: Feature selection by maximum marginal diversity. In: Proc. CVPR, pp. 762–769 (2003)
Wang, J.Z., et al.: Content-based image indexing and searching using Daubechies’ wavelets. IJDL 1, 311–328 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tong, H., Li, M., Zhang, HJ., He, J., Zhang, C. (2004). Classification of Digital Photos Taken by Photographers or Home Users. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30541-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-30541-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23974-1
Online ISBN: 978-3-540-30541-5
eBook Packages: Computer ScienceComputer Science (R0)