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    Natural scene classification, annotation and retrieval. Developing different approaches for semantic scene modelling based on Bag of Visual Words.

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    PhD_Thesis_Yousef_Alqasrawi_06026725.pdf (8.339Mb)
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    Publication date
    2013-04-05
    Author
    Alqasrawi, Yousef T. N.
    Supervisor
    Neagu, Daniel
    Cowling, Peter I.
    Keyword
    Image classification
    Image retrieval
    Bag of visual words
    Visual vocabulary
    Features fusion
    Spatial pyramid layout
    Concept-based bag of visual words
    Digital images
    Natural scene classification
    Keypoints density-based weighting method (KDW)
    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    Department of Computing
    Awarded
    2012
    
    Metadata
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    Abstract
    With the availability of inexpensive hardware and software, digital imaging has become an important medium of communication in our daily lives. A huge amount of digital images are being collected and become available through the internet and stored in various fields such as personal image collections, medical imaging, digital arts etc. Therefore, it is important to make sure that images are stored, searched and accessed in an efficient manner. The use of bag of visual words (BOW) model for modelling images based on local invariant features computed at interest point locations has become a standard choice for many computer vision tasks. Based on this promising model, this thesis investigates three main problems: natural scene classification, annotation and retrieval. Given an image, the task is to design a system that can determine to which class that image belongs to (classification), what semantic concepts it contain (annotation) and what images are most similar to (retrieval). This thesis contributes to scene classification by proposing a weighting approach, named keypoints density-based weighting method (KDW), to control the fusion of colour information and bag of visual words on spatial pyramid layout in a unified framework. Different configurations of BOW, integrated visual vocabularies and multiple image descriptors are investigated and analyzed. The proposed approaches are extensively evaluated over three well-known scene classification datasets with 6, 8 and 15 scene categories using 10-fold cross validation. The second contribution in this thesis, the scene annotation task, is to explore whether the integrated visual vocabularies generated for scene classification can be used to model the local semantic information of natural scenes. In this direction, image annotation is considered as a classification problem where images are partitioned into 10x10 fixed grid and each block, represented by BOW and different image descriptors, is classified into one of predefined semantic classes. An image is then represented by counting the percentage of every semantic concept detected in the image. Experimental results on 6 scene categories demonstrate the effectiveness of the proposed approach. Finally, this thesis further explores, with an extensive experimental work, the use of different configurations of the BOW for natural scene retrieval.
    URI
    http://hdl.handle.net/10454/5523
    Type
    Thesis
    Qualification name
    PhD
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    Theses

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