JOURNAL BROWSE
Search
Advanced SearchSearch Tips
An approach for improving the performance of the Content-Based Image Retrieval (CBIR)
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
An approach for improving the performance of the Content-Based Image Retrieval (CBIR)
Jeong, Inseong;
 
 Abstract
Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.
 Keywords
Content-Based Image Retrieval;feature vector;texture descriptor;similarity metric;
 Language
English
 Cited by
1.
An Approach for the Cross Modality Content-Based Image Retrieval between Different Image Modalities,;;

한국측량학회지, 2013. vol.31. 6_2, pp.585-592 crossref(new window)
 References
1.
Bhagavathy, S. (2005), Modeling and Detection of Geospatial Objects Using Texture Motifs, PhD dissertation, University of California, Santa Barbara

2.
Conners, R. and Harlow C. C. (1980), A theoretical comparison of texture algorithms, IEEE Transaction on Pattern Analysis and Machine Intelligence, IEEE, Vol. 2, No. 3, pp. 204-222.

3.
Emerson, C., Quattrochi D. and Siu-Ngan N. (2004), Spatial Metadata for Remote Sensing Imagery, NASA 14th Earth Science Technology Conference (ESTC), Palo Alto, CA

4.
Haralick, R. M., Shanmugam, K. and Dinstein, I. (1973), Textural features for image classification, IEEE Transactions on Systems, Man, and Cybernetics SMC-3, pp. 610-621.

5.
Li, J. and Narayanan, R. M. (2004), Integrated spectral and spatial information mining in remote sensing imagery, IEEE Transactionson Geoscience and Remote Sensing, IEEE, Vol. 42, No. 3, pp. 673-685. crossref(new window)

6.
Newsam, S., Wang L., Bhagavathy, S. and Manjunath, B. S. (2004), Using texture to analyze and manage large collections of remote sensed image and video data, Journal of Applied Optics: Information Processing, Vol. 43, No. 2, pp. 210-217.

7.
Ohm, J. R., Bunjamin, F., Liebsch, W., Makai, B., Muller, K., Smolic, A. and Zier, D. (2000), A set of visual feature descriptors and their combination in a lowlevel description scheme, Signal Processing: Image Communication., Vol. 16, pp. 157-179. crossref(new window)

8.
Streilein, W. W., Waxman, A., Ross, W. D., Liu, F., Braun, M., Fay, D., Harmon, P. and Read, C. H. (2000), Fused Multi-Sensor Image Mining for Feature Foundation Data, Proceedings of the 3rd International Conference on Information Fusion, vol. 1.