Region based image retrieval incorporated with camera metadata

Authors:
Jie Ma;Hong Lu;Yue-Fei Guo
Affiliations:
Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China;Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China;Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China
Venue:
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Year:
2007

Citing 5
Cited 0

Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Metric for Distributions with Applications to Image Databases

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Region-Based Image Retrieval with High-Level Semantic Color Names

MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Bayesian fusion of camera metadata cues in semantic scene classification

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Content based image retrieval (CBIR) has been researched for decades. However, the "semantic gap" which exists between low-level features and human semantics still remains an unsolved problem. Region based image retrieval (RBIR) was proposed to bridge this gap in some extent. Beyond the pixel values in the image, what other information can also be used? The other information we use is Exif, which records the snapping condition of camera metadata. In this paper we propose an method that combines region low level features of image and camera metadata for image retrieval. Experimental results show the efficiency of our method than the traditional CBIR.