Discriminative kernel feature extraction and learning for object recognition and detection

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

Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature learning, we develop a novel codebook learning method, based on the Cauchy-Schwarz Quadratic Mutual Information (CSQMI) measure, to learn a compact and discriminative CKD codebook from a rich and redundant CKD dictionary. Projecting the original full-dimensional CKD onto the codebook, we reduce the dimensionality of CKD without losing its discriminability. CSQMI derived from Rényi quadratic entropy can be efficiently estimated using a Parzen window estimator even in high-dimensional space. In addition, the latent connection between Rényi quadratic entropy and the mapping data in kernel feature space further facilitates us to capture the geometric structure as well as the information about the underlying labels of the CKD using CSQMI. Thus the resulting codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Pattern Recognition Applications and Methods
EditorsMaria De Marsico, Mário Figueiredo, Ana Fred
Number of pages11
Volume1
PublisherSCITEPRESS Digital Library
Publication date2015
Pages99-109
ISBN (Electronic)978-989-758-076-5
DOIs
Publication statusPublished - 2015
Event4th International Conference on Pattern Recognition: Applications and Methods - Lissabon, Portugal
Duration: 10 Jan 201512 Jan 2015
Conference number: 4

Conference

Conference4th International Conference on Pattern Recognition: Applications and Methods
Nummer4
LandPortugal
ByLissabon
Periode10/01/201512/01/2015

    Research areas

  • Faculty of Science - Context kernel descriptors, Cauchy-Schwarz Quadratic Mutual Information, Feature extraction and learning, Object recognition and detection

ID: 127884069