In this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. The precision-recall measure and the correct classification rate of the k-NN classifier are used to evaluate retrieval and classification performances, respectively. The experimental results for a database of 1000 images from 10 different semantic groups, based on color histogram, directional edge histogram and Gabor features are presented and discussed.
Nezamabadi-Pour, H. & Kabir, E. (2005). » Research Note « Evaluation of Dissimilarity Measures for Image Retrieval and Classification. The Modares Journal of Electrical Engineering, 5(0), 89–98.
MLA
Nezamabadi-Pour, H., & Kabir, E. "» Research Note « Evaluation of Dissimilarity Measures for Image Retrieval and Classification", The Modares Journal of Electrical Engineering, 5, 0, 2005, 89–98.
HARVARD
Nezamabadi-Pour, H., & Kabir, E. (2005). '» Research Note « Evaluation of Dissimilarity Measures for Image Retrieval and Classification', The Modares Journal of Electrical Engineering, 5(0), pp. 89–98.
CHICAGO
Nezamabadi-Pour, H. & Kabir, E., "» Research Note « Evaluation of Dissimilarity Measures for Image Retrieval and Classification." The Modares Journal of Electrical Engineering, 5 0 (2005): 89–98,
VANCOUVER
Nezamabadi-Pour H., Kabir E. » Research Note « Evaluation of Dissimilarity Measures for Image Retrieval and Classification. The Modares Journal of Electrical Engineering. 2005;5(0):89–98.
The Modares Journal of Electrical Engineering