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Efstathios Hadjidemetriou, Michael D. Grossberg, Shree K. Nayar, "Multiresolution Histograms and Their Use for Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 7, pp. 831847, July, 2004.  
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@article{ 10.1109/TPAMI.2004.32, author = {Efstathios Hadjidemetriou and Michael D. Grossberg and Shree K. Nayar}, title = {Multiresolution Histograms and Their Use for Recognition}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {26}, number = {7}, issn = {01628828}, year = {2004}, pages = {831847}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.32}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Multiresolution Histograms and Their Use for Recognition IS  7 SN  01628828 SP831 EP847 EPD  831847 A1  Efstathios Hadjidemetriou, A1  Michael D. Grossberg, A1  Shree K. Nayar, PY  2004 KW  Multiresolution histogram KW  scalespace KW  image sharpness KW  Fisher information KW  shape feature KW  texture feature KW  histogram matching KW  histogram bin width KW  feature parameter sensitivity KW  feature comparison. VL  26 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—The histogram of image intensities is used extensively for recognition and for retrieval of images and video from visual databases. A single image histogram, however, suffers from the inability to encode spatial image variation. An obvious way to extend this feature is to compute the histograms of multiple resolutions of an image to form a multiresolution histogram. The multiresolution histogram shares many desirable properties with the plain histogram including that they are both fast to compute, space efficient, invariant to rigid motions, and robust to noise. In addition, the multiresolution histogram directly encodes spatial information. We describe a simple yet novel matching algorithm based on the multiresolution histogram that uses the differences between histograms of consecutive image resolutions. We evaluate it against five widely used image features. We show that with our simple feature we achieve or exceed the performance obtained with more complicated features. Further, we show our algorithm to be the most efficient and robust.
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