TY - GEN
T1 - Machine learning for image classification and clustering using a universal distance measure
AU - Chester, Uzi
AU - Ratsaby, Joel
PY - 2013
Y1 - 2013
N2 - We present a new method for image feature-extraction which is based on representing an image by a finite-dimensional vector of distances that measure how different the image is from a set of image prototypes. We use the recently introduced Universal Image Distance (UID) [1] to compare the similarity between an image and a prototype image. The advantage in using the UID is the fact that no domain knowledge nor any image analysis need to be done. Each image is represented by a finite dimensional feature vector whose components are the UID values between the image and a finite set of image prototypes from each of the feature categories. The method is automatic since once the user selects the prototype images, the feature vectors are automatically calculated without the need to do any image analysis. The prototype images can be of different size, in particular, different than the image size. Based on a collection of such cases any supervised or unsupervised learning algorithm can be used to train and produce an image classifier or image cluster analysis. In this paper we present the image feature-extraction method and use it on several supervised and unsupervised learning experiments for satellite image data. The feature-extraction method is scalable and is easily implementable on multi-core computing resources.
AB - We present a new method for image feature-extraction which is based on representing an image by a finite-dimensional vector of distances that measure how different the image is from a set of image prototypes. We use the recently introduced Universal Image Distance (UID) [1] to compare the similarity between an image and a prototype image. The advantage in using the UID is the fact that no domain knowledge nor any image analysis need to be done. Each image is represented by a finite dimensional feature vector whose components are the UID values between the image and a finite set of image prototypes from each of the feature categories. The method is automatic since once the user selects the prototype images, the feature vectors are automatically calculated without the need to do any image analysis. The prototype images can be of different size, in particular, different than the image size. Based on a collection of such cases any supervised or unsupervised learning algorithm can be used to train and produce an image classifier or image cluster analysis. In this paper we present the image feature-extraction method and use it on several supervised and unsupervised learning experiments for satellite image data. The feature-extraction method is scalable and is easily implementable on multi-core computing resources.
UR - http://www.scopus.com/inward/record.url?scp=84886432142&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41062-8_7
DO - 10.1007/978-3-642-41062-8_7
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AN - SCOPUS:84886432142
SN - 9783642410611
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 59
EP - 72
BT - Similarity Search and Applications - 6th International Conference, SISAP 2013, Proceedings
T2 - 6th International Conference on Similarity Search and Applications, SISAP 2013
Y2 - 2 October 2013 through 4 October 2013
ER -