IEEE Transactions on Pattern Analysis and Machine Intelligence
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Isomap Based on the Image Euclidean Distance
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Enhancing Training Set for Face Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Robust Hausdorff distance measure for face recognition
Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Optimized image resizing using seam carving and scaling
ACM SIGGRAPH Asia 2009 papers
Selecting optimal orientations of Gabor wavelet filters for facial image analysis
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Hierarchical kernel-based rotation and scale invariant similarity
Pattern Recognition
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The image Euclidean distance (IMED) considers the spatial relationship between the pixels of different images and can easily be embedded in existing image recognition algorithms that are based on Euclidean distance. IMED uses the prior knowledge that pixels located near one another have little variance in gray scale values, and defines a metric matrix according to the spatial distance between pixels. In this paper, we propose an adaptive image Euclidean distance (AIMED), which considers not only the prior spatial knowledge, but also the prior gray level knowledge from images. The most important advantage of the proposed AIMED over IMED is that AIMED makes the metric matrix adaptive to the content of the concerned images. Two ways of using gray level information are proposed. One is based on gray level distances, and the other is based on cosine dissimilarity of gray levels. Experiments on two facial databases and a handwritten digital database show that AIMED achieves the highest classification accuracy when it is embedded in nearest neighbor classifiers, principal component analysis, and support vector machines.