Image feature associations via local semantic structure
| dc.contributor.author | Parrish, Nicholas James, author | |
| dc.contributor.author | Draper, Bruce A., advisor | |
| dc.contributor.author | Beveridge, Ross, committee member | |
| dc.contributor.author | Troup, Lucy, committee member | |
| dc.date.accessioned | 2007-01-03T04:52:29Z | |
| dc.date.available | 2007-01-03T04:52:29Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | Research in the field of object recognition suffers from two distinct weaknesses that limits its effectiveness in natural environments. The first is that this research tends to rely on labeled training images, or other forms of supervision, to learn object models and recognize these models in novel images, thus preventing the learning of objects that are not labeled by humans. The second is that such systems tend to assume that the goal is to recognize a single, dominant foreground object. This research implements a different method of object recognition that learns, with- out supervision, which object(s) are in natural scenes. This approach uses the semantic co-occurance information of local image features to form object models from groups of image features, which shall be called percepts. These percepts are then used to recognize objects in novel images. It will be shown that this approach is capable of learning object categories without supervision and recognition in complex multi-object scenes. It will also be shown that this approach out-performs a nearest-neighbor scene recognition approach. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Parrish_colostate_0053N_10124.pdf | |
| dc.identifier | ETDF2010200120COMS | |
| dc.identifier.uri | http://hdl.handle.net/10217/44971 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | Image processing | |
| dc.subject | Perceptrons | |
| dc.subject | Semantic computing | |
| dc.title | Image feature associations via local semantic structure | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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