On image classification: city vs. landscape

@inproceedings{citeulike:2568746, abstract = {Grouping images into semantically meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, effective indices can be built for an image database. The authors show how a specific high-level classification problem (city vs. landscape classification) can be solved from relatively simple low-level features suited for the particular classes. They have developed a procedure to qualitatively measure the saliency of a feature for classification problem based on the plot of the intra-class and inter-class distance distributions. They use this approach to determine the discriminative power of the following features: color histogram, color coherence vector DCT coefficient, edge direction histogram, and edge direction coherence vector. They determine that the edge direction-based features have the most discriminative power for the classification problem of interest. A weighted k-NN classifier is used for the classification. The classification system results in an accuracy of 93.9\% when evaluated on an image database of 2,716 images using the leave-one-out method}, author = {Vailaya, A. and Jain, A. and Zhang, Hong J. }, booktitle = {Content-Based Access of Image and Video Libraries, 1998. Proceedings. IEEE Workshop on}, citeulike-article-id = {2568746}, comment = {CBIR application paper: classify urban vs. rural photographs.}, doi = {http://dx.doi.org/10.1109/IVL.1998.694464}, journal = {Content-Based Access of Image and Video Libraries, 1998. Proceedings. IEEE Workshop on}, keywords = {cbir}, pages = {3--8}, posted-at = {2008-03-21 04:35:55}, priority = {2}, title = {On image classification: city vs. landscape}, url = {http://dx.doi.org/10.1109/IVL.1998.694464}, year = {1998} }

TY - CONF ID - citeulike:2568746 L3 - citeulike-article-id:2568746 TI - On image classification: city vs. landscape T2 - Content-Based Access of Image and Video Libraries, 1998. Proceedings. IEEE Workshop on JF - Content-Based Access of Image and Video Libraries, 1998. Proceedings. IEEE Workshop on SP - 3 EP - 8 N2 - Grouping images into semantically meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, effective indices can be built for an image database. The authors show how a specific high-level classification problem (city vs. landscape classification) can be solved from relatively simple low-level features suited for the particular classes. They have developed a procedure to qualitatively measure the saliency of a feature for classification problem based on the plot of the intra-class and inter-class distance distributions. They use this approach to determine the discriminative power of the following features: color histogram, color coherence vector DCT coefficient, edge direction histogram, and edge direction coherence vector. They determine that the edge direction-based features have the most discriminative power for the classification problem of interest. A weighted k-NN classifier is used for the classification. The classification system results in an accuracy of 93.9% when evaluated on an image database of 2,716 images using the leave-one-out method KW - cbir N1 - CBIR application paper: classify urban vs. rural photographs. AU - Vailaya, A AU - Jain, A AU - Zhang, Hong PY - 1998/// UR - http://dx.doi.org/10.1109/IVL.1998.694464 ER -