Consistent line clusters for building recognition in CBIR

This paper introduces a new mid-level feature, the consistent line cluster for use in content-based image retrieval. The color, orientation, and spatial features of line segments are exploited to group them into line clusters. The interrelationships among different clusters and the intra relationships within single clusters are used to recognize and roughly locate buildings in photographic images. Experiments are performed on a database of color images of outdoor scenes.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  A. Etemadi.  Robust segmentation of edge data , 1992 .

[3]  Alberto Del Bimbo,et al.  Visual image retrieval by elastic deformation of object sketches , 1994, Proceedings of 1994 IEEE Symposium on Visual Languages.

[4]  David A. Forsyth,et al.  Body plans , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Serge J. Belongie,et al.  Region-based image querying , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[6]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[7]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[8]  Anil K. Jain,et al.  On image classification: city images vs. landscapes , 1998, Pattern Recognit..

[9]  Jake K. Aggarwal,et al.  Applying perceptual grouping to content-based image retrieval: building images , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[10]  Yihong Gong.  Advancing content-based image retrieval by exploiting image color and region features , 1999, Multimedia Systems.