Object classification and detection with context kernel descriptors

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Context information is important in object representation. By embedding context cue of image attributes into kernel descriptors, we propose a set of novel kernel descriptors called Context Kernel Descriptors (CKD) for object classification and detection. The motivation of CKD is to use spatial consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature selection, Kernel Entropy Component Analysis (KECA) is exploited to learn a subset of discriminative CKD. Different from Kernel Principal Component Analysis (KPCA) that only keeps features contributing mostly to image reconstruction, KECA selects the CKD that contribute mostly to the Rényi entropy of the image. These CKD are discriminative as they relate to the density distribution of the histogram of image attributes. We report superior performance of CKD for object classification on the CIFAR-10 dataset, and for detection on a challenging chicken feet dataset.
Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 19th Iberoamerican Congress, CIARP 2014, Puerto Vallarta, Mexico, November 2-5, 2014. Proceedings
EditorsEduardo Bayro-Corrochano, Edwin Hancock
Number of pages9
Publication date2014
ISBN (Print)978-3-319-12567-1
Publication statusPublished - 2014
EventIberoamerican Congress 2014 - Puerto Vallarta, Mexico
Duration: 2 Nov 20145 Nov 2014
Conference number: 19


ConferenceIberoamerican Congress 2014
ByPuerto Vallarta
SeriesLecture notes in computer science

    Research areas

  • Faculty of Science - Object classification and detection, Feature selection, Kernel descriptors, Kernel entropy component analysis

ID: 127191961