The objective of the present work is to propose a novel method to extract a stable feature set representative of image content. Each image is represented by a linear combination of fractal orthonormal basis vectors. The mapping coefficients of an image projected onto each orthonormal basis constitute the feature vector. The set of orthonormal basis vectors are generated by utilizing fractal iterative function through target and domain blocks mapping. The distance measure remains consistent, i.e., isometric embedded, between any image pairs before and after the projection onto orthonormal axes. Not only similar images generate points close to each other in the feature space, but also dissimilar ones produce feature points far apart. The above statements are logically equivalent to that distant feature points are guaranteed to map to images with dissimilar contents, while close feature points correspond to similar images. Therefore, utilizing coefficients derived from the proposed linear combination of fractal orthonormal basis as key to search image database will retrieve similar images, while at the same time exclude dissimilar ones. The coefficients associated with each image can be later used to reconstruct the original. The content-based query is performed in the compressed domain. This approach is efficient for content-based query. Scaling, rotational, translation, mirroring and horizontal/vertical flipping variations of a query image are also supported.
A symbolic image database system is a system in which a large amount of image data and their related information are represented by both symbolic images and physical images. How to perceive spatial relationships among the components in a symbolic image is an important criterion to find a match between the symbolic image of the scene object and the one being store as a modal in the symbolic image database. Spatial reasoning techniques have been applied to pictorial database, in particular those using 2D strings as an index representation have been successful. In most of the previous approaches for iconic indexing, for simplifying the concerns, they apply the MBR (Minimum bounding rectangle) of two objects to define the spatial relationship between them.
Multiple instance learning algorithms provide ways for computer program to improve automatically with experience. Most images are inherently ambiguous disseminators of information. Unfortunately, interfaces to image databases normally involve the user giving the system ambiguous queries. By treating each query as a Multiple-Instance example, we make the ambiguity in each image explicit. In addition, by receiving several positive and negative examples, the system can learn what the user desires. Using the learned concept, the system returns images from the database that are close to that concept. In this project, we propose to apply the Multiple-Instance learning model by deriving the projection vector of fractal orthonormal bases for a small number of training images to learn what images from database are of interest to the user.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0701106-154958 |
| Date | 01 July 2006 |
| Creators | Lai, Chin-Ning |
| Contributors | none, Chungnan Lee, John-Y Chiang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0701106-154958 |
| Rights | restricted, Copyright information available at source archive |
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