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The Earth Mover's Distance as a Metric for Image Retrieval
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IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical evaluation of dissimilarity measures for color and texture
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Texture Recognition Using Bidirectional Feature Histograms
International Journal of Computer Vision
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Diffusion Distance for Histogram Comparison
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Proceedings of the 24th international conference on Machine learning
Distance Learning for Similarity Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Linear Time Histogram Metric for Improved SIFT Matching
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
A Statistical Approach to Material Classification Using Image Patch Exemplars
IEEE Transactions on Pattern Analysis and Machine Intelligence
The quadratic-chi histogram distance family
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Near Duplicate Identification With Spatially Aligned Pyramid Matching
IEEE Transactions on Circuits and Systems for Video Technology
The quadratic-chi histogram distance family
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Computer Vision and Image Understanding
GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures
International Journal of Computational Vision and Robotics
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Proceedings of the Third Symposium on Information and Communication Technology
A universal technique to hide traces of histogram-based image manipulations
Proceedings of the on Multimedia and security
Local fuzzy pattern: a new way for micro-pattern analysis
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Computer Graphics Forum
treeKL: A distance between high dimension empirical distributions
Pattern Recognition Letters
International Journal of Computer Vision
A hierarchical semantic-based distance for nominal histogram comparison
Data & Knowledge Engineering
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We present a new histogram distance family, the Quadratic-Chi (QC). QC members are Quadratic-Form distances with a cross-bin χ2-like normalization. The cross-bin χ2-like normalization reduces the effect of large bins having undo influence. Normalization was shown to be helpful in many cases, where the χ2 histogram distance outperformed the L2 norm. However, χ2 is sensitive to quantization effects, such as caused by light changes, shape deformations etc. The Quadratic-Form part of QC members takes care of cross-bin relationships (e.g. red and orange), alleviating the quantization problem. We present two new crossbin histogram distance properties: Similarity-Matrix-Quantization-Invariance and Sparseness-Invariance and show that QC distances have these properties.We also show that experimentally they boost performance. QC distances computation time complexity is linear in the number of non-zero entries in the bin-similarity matrix and histograms and it can easily be parallelized.We present results for image retrieval using the Scale Invariant Feature Transform (SIFT) and color image descriptors. In addition, we present results for shape classification using Shape Context (SC) and Inner Distance Shape Context (IDSC). We show that the new QC members outperform state of the art distances for these tasks, while having a short running time. The experimental results show that both the cross-bin property and the normalization are important.