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Evaluating cluster quality for visual data

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dc.contributor.authorWigness, Maggie, author
dc.contributor.authorDraper, Bruce, advisor
dc.contributor.authorBeveridge, Ross, committee member
dc.contributor.authorHowe, Adele, committee member
dc.contributor.authorPeterson, Chris, committee member
dc.date.accessioned2007-01-03T05:34:08Z
dc.date.available2007-01-03T05:34:08Z
dc.date.issued2013
dc.description.abstractDigital video cameras have made it easy to collect large amounts of unlabeled data that can be used to learn to recognize objects and actions. Collecting ground-truth labels for this data, however, is a much more time consuming task that requires human intervention. One approach to train on this data, while keeping the human workload to a minimum, is to cluster the unlabeled samples, evaluate the quality of the clusters, and then ask a human annotator to label only the clusters believed to be dominated by a single object/action class. This thesis addresses the task of evaluating the quality of unlabeled image clusters. We compare four cluster quality measures (and a baseline method) using real-world and synthetic data sets. Three of these measures can be found in the existing data mining literature: Dunn Index, Davies-Bouldin Index and Silhouette Width. We introduce a novel cluster quality measure as the fourth measure, derived from recent advances in approximate nearest neighbor algorithms from the computer vision literature, called Proximity Forest Connectivity (PFC). Experiments on real-world data show that no cluster quality measure performs "best" on all data sets; however, our novel PFC measure is always competitive and results in more top performances than any of the other measures. Results from synthetic data experiments show that while the data mining measures are susceptible to over-clustering typically required of visual data, PFC is much more robust. Further synthetic data experiments modeling features of visual data show that Davies-Bouldin is most robust to large amounts of class-specific noise. However, Davies-Bouldin, Silhouette and PFC all perform well in the presence of data with small amounts of class-specific noise, whereas Dunn struggles to perform better than random.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierWigness_colostate_0053N_11728.pdf
dc.identifier.urihttp://hdl.handle.net/10217/79204
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright 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.subjectcluster quality measures
dc.subjectimage clustering
dc.subjectcomputer vision
dc.titleEvaluating cluster quality for visual data
dc.typeText
dcterms.rights.dplaThis 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.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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