Automatic Image Annotation for Semantic Image Retrieval

This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our analysis shows that the colour structure and edge histogram descriptors perform best, compared to a wide range of MPEG-7 visual descriptors. On a dataset of 7200 landscape and cityscape images representing real-life varied quality and resolution, the MPEG-7 colour structure descriptor and edge histogram descriptor achieve a classification rate of 82.8% and 84.6%, respectively. By combining these two features, we are able to achieve a classification rate of 89.7%. Our results demonstrate that combining salient features can significantly improve classification of images.

[1]  Shigeo Abe,et al.  Support Vector Machines for Pattern Classification (Advances in Pattern Recognition) , 2005 .

[2]  Fuhui Long,et al.  Fundamentals of Content-Based Image Retrieval , 2003 .

[3]  Anil K. Jain,et al.  Content-based hierarchical classification of vacation images , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[4]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Anil K. Jain,et al.  On image classification: city vs. landscape , 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173).

[6]  Rainer Lienhart,et al.  Classifying images on the web automatically , 2002, J. Electronic Imaging.

[7]  Elaine C. Yiu.  Image classification using color cues and texture orientation , 1996 .

[8]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[9]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[10]  B. S. Manjunath,et al.  Introduction to MPEG-7: Multimedia Content Description Interface , 2002 .

[11]  JainRamesh,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000 .

[12]  Martin Szummer,et al.  Indoor-outdoor image classification , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[13]  James Ze Wang,et al.  Content-based image retrieval: approaches and trends of the new age , 2005, MIR '05.

[14]  John P. Eakins,et al.  Retrieval of Still Images by Content , 2000, ESSIR.

[15]  Frank Nack,et al.  Everything You Wanted to Know About MPEG-7: Part 2 , 1999, IEEE Multim..

[16]  Frank Nack,et al.  Everything You Wanted to Know About MPEG-7: Part 1 , 1999, IEEE Multim..

[17]  Chun Chen,et al.  A novel Bayesian framework for indoor-outdoor image classification , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).