Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
Hanolistic : a hierarchical automatic image annotation system using holistic approach
Download
index.pdf
Date
2008
Author
Öztimur, Özge
Metadata
Show full item record
Item Usage Stats
2
views
8
downloads
Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this thesis, we propose a novel approach for automatic image annotation problem, where the annotation is formulated as a multivariate mapping from a set of independent descriptor spaces, representing a whole image, to a set of words, representing class labels. For this purpose, a hierarchical annotation architecture, named as HANOLISTIC (Hierarchical Image Annotation System Using Holistic Approach), is de ned with two layers. At the rst layer, called level-0 annotator, each annotator is fed by a set of distinct descriptor, extracted from the whole image. This enables us to represent the image at each annotator by a di erent visual property of a descriptor. Since, we use the whole image, the problematic segmentation process is avoided. Training of each annotator is accomplished by a supervised learning paradigm, where each word is represented by a class label. Note that, this approach is slightly di erent then the classical training approaches, where each data has a unique label. In the proposed system, since each image has one or more annotating words, we assume that an image belongs to more than one class. The output of the level-0 annotators indicate the membership values of the words in the vocabulary, to belong an image. These membership values from each annotator is, then, aggregated at the second layer by using various rules, to obtain meta-layer annotator. The rules, employed in this study, involves summation and/or weighted summation of the output of layer-0 annotators. Finally, a set of words from the vocabulary is selected based on the ranking of the output of meta-layer. The hierarchical annotation system proposed in this thesis outperforms state of the art annotation systems based on segmental and holistic approaches. The proposed system is examined in-depth and compared to the other systems in the literature by means of using several performance criteria.
Subject Keywords
Computer enginnering.
URI
http://etd.lib.metu.edu.tr/upload/3/12609230/index.pdf
https://hdl.handle.net/11511/17668
Collections
Graduate School of Natural and Applied Sciences, Thesis