Color sets with morphological and B-spline enhancements for content-based image retrieval
AuthorMlsna, Phillip Anthony
AdvisorRodriguez, Jeffrey J.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractDatabases of color images have become increasingly important in recent years. The text-based retrieval of images in such databases is practical only if descriptive text annotations accompany each image. Creating such text descriptions is a labor intensive process requiring human interpretation of each image. Except for relatively small, static collections of images, the cost of generating text annotations is prohibitive. The desire to avoid the use of text-based image descriptors has therefore led to the investigation of feature-based descriptors that can automatically be extracted from images and indexed in the database. The definitions of these features and the algorithms for their automated extraction from a given image are the foci of most of the current research into Content-Based Image Retrieval (CBIR). This work focuses on improving the computational efficiency of the well-known color set algorithm for content-based image retrieval. The color set concept is a useful and efficient approach to image indexing and query in a way that combines color and spatial information. By indexing relatively important regions based on both their color content and their spatial locations, the color set method allows rapid retrieval of images matching a specified color-spatial query. Several refinements of the color set approach are presented in this dissertation. First, the process of determining the relevant color combinations for color sets to be indexed has been made approximately one to two orders of magnitude more efficient than the original algorithm. Second, a B-spline descriptor of region shape, size, and position has been incorporated to supplement the original method's rectangular bounding box. Describing contours as closed B-spline curves provides an accurate and storage-efficient means of indexing region location and shape. During image query, the convex hull property of B-spline curves is exploited to enable efficient determination of region containment of a specific point. Finally, an idea for improving the computational efficiency of approximating region contours with periodic, quadratic B-spline curves is briefly discussed.
Degree ProgramGraduate College
Electrical and Computer Engineering