File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Beyond spatial pyramids: A new feature extraction framework with dense spatial sampling for image classification

TitleBeyond spatial pyramids: A new feature extraction framework with dense spatial sampling for image classification
Authors
KeywordsAdapted Classifier
Image Classification
Multiple Kernel Learning
Sliding Window
Spatial Pyramid
Issue Date2012
PublisherSpringer
Citation
12th European Conference on Computer Vision (ECCV 2012), Florence, Italy, 7-13 October 2012. In Fitzgibbon, A, Lazebnik, S, Perona, P, et al. (Eds.), Computer Vision - ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012. Proceedings, Part IV, p. 473-487. Berlin: Springer, 2012 How to Cite?
AbstractWe introduce a new framework for image classification that extends beyond the window sampling of fixed spatial pyramids to include a comprehensive set of windows densely sampled over location, size and aspect ratio. To effectively deal with this large set of windows, we derive a concise high-level image feature using a two-level extraction method. At the first level, window-based features are computed from local descriptors (e.g., SIFT, spatial HOG, LBP) in a process similar to standard feature extractors. Then at the second level, the new image feature is determined from the window-based features in a manner analogous to the first level. This higher level of abstraction offers both efficient handling of dense samples and reduced sensitivity to misalignment. More importantly, our simple yet effective framework can readily accommodate a large number of existing pooling/coding methods, allowing them to extract features beyond the spatial pyramid representation. To effectively fuse the second level feature with a standard first level image feature for classification, we additionally propose a new learning algorithm, called Generalized Adaptive ℓ p -norm Multiple Kernel Learning (GA-MKL), to learn an adapted robust classifier based on multiple base kernels constructed from image features and multiple sets of pre-learned classifiers of all the classes. Extensive evaluation on the object recognition (Caltech256) and scene recognition (15Scenes) benchmark datasets demonstrates that the proposed method outperforms state-of-the-art image classification algorithms under a broad range of settings. © 2012 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/321493
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 7575
LNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics

 

DC FieldValueLanguage
dc.contributor.authorYan, Shengye-
dc.contributor.authorXu, Xinxing-
dc.contributor.authorXu, Dong-
dc.contributor.authorLin, Stephen-
dc.contributor.authorLi, Xuelong-
dc.date.accessioned2022-11-03T02:19:16Z-
dc.date.available2022-11-03T02:19:16Z-
dc.date.issued2012-
dc.identifier.citation12th European Conference on Computer Vision (ECCV 2012), Florence, Italy, 7-13 October 2012. In Fitzgibbon, A, Lazebnik, S, Perona, P, et al. (Eds.), Computer Vision - ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012. Proceedings, Part IV, p. 473-487. Berlin: Springer, 2012-
dc.identifier.isbn9783642337642-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/321493-
dc.description.abstractWe introduce a new framework for image classification that extends beyond the window sampling of fixed spatial pyramids to include a comprehensive set of windows densely sampled over location, size and aspect ratio. To effectively deal with this large set of windows, we derive a concise high-level image feature using a two-level extraction method. At the first level, window-based features are computed from local descriptors (e.g., SIFT, spatial HOG, LBP) in a process similar to standard feature extractors. Then at the second level, the new image feature is determined from the window-based features in a manner analogous to the first level. This higher level of abstraction offers both efficient handling of dense samples and reduced sensitivity to misalignment. More importantly, our simple yet effective framework can readily accommodate a large number of existing pooling/coding methods, allowing them to extract features beyond the spatial pyramid representation. To effectively fuse the second level feature with a standard first level image feature for classification, we additionally propose a new learning algorithm, called Generalized Adaptive ℓ p -norm Multiple Kernel Learning (GA-MKL), to learn an adapted robust classifier based on multiple base kernels constructed from image features and multiple sets of pre-learned classifiers of all the classes. Extensive evaluation on the object recognition (Caltech256) and scene recognition (15Scenes) benchmark datasets demonstrates that the proposed method outperforms state-of-the-art image classification algorithms under a broad range of settings. © 2012 Springer-Verlag.-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofComputer Vision - ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012. Proceedings, Part IV-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 7575-
dc.relation.ispartofseriesLNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics-
dc.subjectAdapted Classifier-
dc.subjectImage Classification-
dc.subjectMultiple Kernel Learning-
dc.subjectSliding Window-
dc.subjectSpatial Pyramid-
dc.titleBeyond spatial pyramids: A new feature extraction framework with dense spatial sampling for image classification-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-33765-9_34-
dc.identifier.scopuseid_2-s2.0-84867882770-
dc.identifier.spage473-
dc.identifier.epage487-
dc.identifier.eissn1611-3349-
dc.publisher.placeBerlin-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats