A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Foreground/background segmentation of color images by integration of multiple cues
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Automatic Identification of Perceptually Important Regions in an Image
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Content-based image classification using a neural network
Pattern Recognition Letters
Central object extraction for object-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Global semantic classification of scenes using power spectrum templates
IM'99 Proceedings of the 1999 international conference on Challenge of Image Retrieval
Image classification for content-based indexing
IEEE Transactions on Image Processing
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Recently semantic classification of images is of great interest for image indexing applications. On the one hand, researchers in the field of content-based image retrieval are interested in object(s) of interest in an image, which is useful for representing the image. In this paper, we present a semantic classification method of the object(s) of interest into artificial/natural classes. We first show that dominant orientation features in Gabor filtering results of artificial objects are very useful for discriminating them from natural objects. Dominant orientations in artificial object images are not confined to horizontal and/or vertical directions, while those in artificial scene images tend to be greatly confined to them. Two classification measures are proposed; the sum of sector power differences in Fourier power spectrum and the energy of edge direction histogram. They show classification accuracy of 85.8% and 84.8% on a test with 2,600 object images, respectively.