Title :
An object recognition algorithm using Maximum Margin Correlation Filter and Support Vector Machine
Author :
Bagchi, Saurabh ; Poonacha, P.G.
Author_Institution :
Res. & Technol. Center, Siemens Corp. Technol., Bangalore, India
fDate :
Feb. 28 2014-March 2 2014
Abstract :
We consider the problem of detecting objects in two dimensional images and propose a new technique which uses Support Vector Machine (SVM) along with Maximum Margin Correlation Filter (MMCF). We have shown that our algorithm detects objects well and is robust with respect to scale changes. Introduction of Support Vector Machine (SVM) helps Maximum Margin Correlation Filter (MMCF) to deal with non-linearly separable data also to some extent. The algorithm also detects same object, if it is found several times at several scales, thus it helps avoiding redundant detection of same object and finally selects the best version of it.
Keywords :
filtering theory; object recognition; support vector machines; MMCF; SVM; maximum margin correlation filter; object detection; object recognition algorithm; support vector machine; two dimensional images; Complexity theory; Correlation; Search problems; Support vector machines; Testing; Training; Vectors; Object recognition; maximum margin correlation filter; object localization; support vector machine;
Conference_Titel :
Communications (NCC), 2014 Twentieth National Conference on
Conference_Location :
Kanpur
DOI :
10.1109/NCC.2014.6811272