- author = "Stanton Robert Price",
- title = "Fusion of evolution constructed features for computer vision",
- school = "Electrical and Computer Engineering, Mississippi State University",
- year = "2018",
- type = "Doctor of Philosophy",
- address = "USA",
- month = may # "~07",
- keywords = "genetic algorithms, genetic programming, machine learning, multiple kernel learning, GOOFeD, iECO, image processing, pattern recognition, buried explosive hazard detection, object detection",
- bibsource = "OAI-PMH server at sun.library.msstate.edu",
- language = "en",
- isbn13 = "9780355920727",
- oai = "oai:library.msstate.edu:etd-02052018-212022",
-
URL = "
https://hdl.handle.net/11668/17014",
- broken = "http://sun.library.msstate.edu/ETD-db/theses/available/etd-02052018-212022/",
-
URL = "
http://sun.library.msstate.edu/ETD-db/theses/available/etd-02052018-212022/restricted/PQFINAL.pdf",
-
URL = "https://www.proquest.com/docview/2039435221",
- size = "157 pages",
- abstract = "In this dissertation, image feature extraction quality is enhanced through the introduction of two feature learning techniques and, subsequently, feature-level fusion strategies are presented that improve classification performance. Two image/signal processing techniques are defined for pre-conditioning image data such that the discriminatory information is highlighted for improved feature extraction. The first approach, improved Evolution-COnstructed features, employs a modified genetic algorithm to learn a series of image transforms, specific to a given feature descriptor, for enhanced feature extraction. The second method, Genetic prOgramming Optimal Feature Descriptor (GOOFeD), is a genetic programming-based approach to learning the transformations of the data for feature extraction. GOOFeD offers a very rich and expressive solution space due to is ability to represent highly complex compositions of image transforms through binary, unary, and/or the combination of the two, operators. Regardless of the two techniques employed, the goal of each is to learn a composition of image transforms from training data to present a given feature descriptor with the best opportunity to extract its information for the application at hand. Next, feature-level fusion via multiple kernel learning (MKL) is used to better combine the features extracted and, ultimately, improve classification accuracy performance. MKL is advanced through the introduction of six new indices for kernel weight assignment. Five of the indices are measured directly from the kernel matrix proximity values, making them highly efficient to compute. The calculation of the sixth index is performed explicitly on distributions in the reproducing kernel Hilbert space. The proposed techniques are applied to an automatic buried explosive hazard detection application and significant results are achieved.",
-
notes = "URN etd-02052018-212022 1586997.
Supervisor: John E. Ball
The author has requested that access to this graduate work be delayed until 2019-05-07. After this date, this graduate work will be available on an open access basis.",
