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BIOLOGICALLY INSPIRED OVERCOMPLETE REPRESENTATION, FEATURE
EXTRACTION AND OBJECT CLASSIFICATION
by
Pankaj Mishra
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
August 2011
Copyright 2011 Pankaj Mishra
Object Description
| Title | Biologically inspired overcomplete representation, feature extraction and object classification |
| Author | Mishra, Pankaj |
| Author email | mishra@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-05-09 |
| Date submitted | 2011-06-09 |
| Date approved | 2011-06-09 |
| Restricted until | 2011-06-09 |
| Date published | 2011-06-09 |
| Advisor (committee chair) | Jenkins, B. Keith |
| Advisor (committee member) |
Sawchuk, Alexander A. Schaal, Stefan |
| Abstract | A key to solving the multiclass object recognition problem is to extract a set of features which accurately and uniquely capture the salient characteristics of different objects. We show that complementary kinds of feature sets e.g., based on local, mid-level and global characteristics, can be combined to significantly improve recognition accuracy over that obtained using individual (or subcombination of) feature sets. ❧ First, we extract a set of local features based on a modified HMAX model, which is a hierarchical computational framework inspired by mammalian visual cortex. One of our modifications uses natural-stimuli adapted filters in place of Gabor filters. Overcomplete sets of basis functions based on sparseness maximization criteria have been reported to closely mimic the mammalian visual cortex, V1, in the sense that the resulting basis functions are typically localized, oriented, and bandpass, as are filters in V1. These overcomplete basis functions allow a smooth transition of coefficients and allow a high degree of specificity to image statistics. These natural-stimuli adapted filters are used with the HMAX model which increases its biological plausibility. The resulting features are largely scale, translation and rotation invariant. Second, we extract contextual information using modified Gist and spatial pyramid based features. Third, to capture larger contours and edges we extract features based on the Gestalt principle of continuity in visual perception. ❧ We combine these feature sets using confidence measures derived from discriminative-model based posterior probabilities. Each posterior probability obtained in our case is based on support vector machine (SVM) decision boundaries, in part because SVMs have been shown to do well on large datasets. Different combinations of confidence measures are explored. Most significant improvements are gained using non-trainable fusion techniques. We demonstrate significant improvement for object recognition performance (over individual feature sets) using the publicly available Caltech-101 and 17-species Oxford Flowers datasets. The progressive addition of feature sets always resulted in performance improvement though the incremental gains varied. |
| Keyword | biologically-inspired hierarchical model; fusion of classifiers; object recognition; HMAX; gist features |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Mishra, Pankaj |
| Physical access | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@lib.usc.edu |
| Archival file | uscthesesreloadpub_Volume71/etd-MishraPank-20.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | BIOLOGICALLY INSPIRED OVERCOMPLETE REPRESENTATION, FEATURE EXTRACTION AND OBJECT CLASSIFICATION by Pankaj Mishra A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) August 2011 Copyright 2011 Pankaj Mishra |
