Decomposing Bag of Words Histograms

We aim to decompose a global histogram representation of an image into histograms of its associated objects and regions. This task is formulated as an optimization problem, given a set of linear classifiers, which can effectively discriminate the object categories present in the image. Our decomposi...

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Bibliographic Details
Published in2013 IEEE International Conference on Computer Vision pp. 305 - 312
Main Authors Gandhi, Ankit, Alahari, Karteek, Jawahar, C. V.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Accuracy
Categories
Clutter
Computer vision
Conferences
Decomposition
Histograms
Image classification
Image segmentation
Optimization
Support vector machines
Tasks
Utilities
Visualization
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Summary:We aim to decompose a global histogram representation of an image into histograms of its associated objects and regions. This task is formulated as an optimization problem, given a set of linear classifiers, which can effectively discriminate the object categories present in the image. Our decomposition bypasses harder problems associated with accurately localizing and segmenting objects. We evaluate our method on a wide variety of composite histograms, and also compare it with MRF-based solutions. In addition to merely measuring the accuracy of decomposition, we also show the utility of the estimated object and background histograms for the task of image classification on the PASCAL VOC 2007 dataset.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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SourceType-Conference Papers & Proceedings-2
ISSN:1550-5499
2380-7504
DOI:10.1109/ICCV.2013.45