%0 Journal Article %1 Borenstein2008 %A Borenstein, Eran %A Ullman, Shimon %D 2008 %J IEEE transactions on pattern analysis and machine intelligence %K Algorithms,Artificial Automated,Pattern Automated: Computer-Assisted,Image Computer-Assisted: Enhancement,Image Enhancement: Intelligence,Automated,Automated: Interpretation, Recognition, Results,Sensitivity Specificity,Subtraction Technique and methods,Computer-Assisted,Computer-Assisted: methods,Image methods,Pattern methods,Reproducibility of %N 12 %P 2109--25 %R 10.1109/TPAMI.2007.70840 %T Combined top-down/bottom-up segmentation. %U http://ieeexplore.ieee.org/xpl/freeabs\_all.jsp?arnumber=4408584 %V 30 %X We construct a segmentation scheme that combines top-down with bottom-up processing. In the proposed scheme, segmentation and recognition are intertwined rather than proceeding in a serial manner. The top-down part applies stored knowledge about object shapes acquired through learning, whereas the bottom-up part creates a hierarchy of segmented regions based on uniformity criteria. Beginning with unsegmented training examples of class and non-class images, the algorithm constructs a bank of class-specific fragments and determines their figure-ground segmentation. This bank is then used to segment novel images in a top-down manner: the fragments are first used to recognize images containing class objects, and then to create a complete cover that best approximates these objects. The resulting segmentation is then integrated with bottom-up multi-scale grouping to better delineate the object boundaries. Our experiments, applied to a large set of four classes (horses, pedestrians, cars, faces), demonstrate segmentation results that surpass those achieved by previous top-down or bottom-up schemes. The main novel aspects of this work are the fragment learning phase, which efficiently learns the figure-ground labeling of segmentation fragments, even in training sets with high object and background variability; combining the top-down segmentation with bottom-up criteria to draw on their relative merits; and the use of segmentation to improve recognition.

@article{Borenstein2008, abstract = {We construct a segmentation scheme that combines top-down with bottom-up processing. In the proposed scheme, segmentation and recognition are intertwined rather than proceeding in a serial manner. The top-down part applies stored knowledge about object shapes acquired through learning, whereas the bottom-up part creates a hierarchy of segmented regions based on uniformity criteria. Beginning with unsegmented training examples of class and non-class images, the algorithm constructs a bank of class-specific fragments and determines their figure-ground segmentation. This bank is then used to segment novel images in a top-down manner: the fragments are first used to recognize images containing class objects, and then to create a complete cover that best approximates these objects. The resulting segmentation is then integrated with bottom-up multi-scale grouping to better delineate the object boundaries. Our experiments, applied to a large set of four classes (horses, pedestrians, cars, faces), demonstrate segmentation results that surpass those achieved by previous top-down or bottom-up schemes. The main novel aspects of this work are the fragment learning phase, which efficiently learns the figure-ground labeling of segmentation fragments, even in training sets with high object and background variability; combining the top-down segmentation with bottom-up criteria to draw on their relative merits; and the use of segmentation to improve recognition.}, added-at = {2013-09-29T14:16:50.000+0200}, author = {Borenstein, Eran and Ullman, Shimon}, biburl = {https://www.bibsonomy.org/bibtex/2d8814997ca9190ce7bba5f0dd93241b6/guillem.palou}, doi = {10.1109/TPAMI.2007.70840}, interhash = {5bae61723abc32d1263a5c2925f05502}, intrahash = {d8814997ca9190ce7bba5f0dd93241b6}, issn = {0162-8828}, journal = {IEEE transactions on pattern analysis and machine intelligence}, keywords = {Algorithms,Artificial Automated,Pattern Automated: Computer-Assisted,Image Computer-Assisted: Enhancement,Image Enhancement: Intelligence,Automated,Automated: Interpretation, Recognition, Results,Sensitivity Specificity,Subtraction Technique and methods,Computer-Assisted,Computer-Assisted: methods,Image methods,Pattern methods,Reproducibility of}, number = 12, pages = {2109--25}, pmid = {18988946}, timestamp = {2013-09-29T14:16:50.000+0200}, title = {{Combined top-down/bottom-up segmentation.}}, url = {http://ieeexplore.ieee.org/xpl/freeabs\_all.jsp?arnumber=4408584}, volume = 30, year = 2008 }