Over the last decade, storage of non text-based data in databases has become an increasingly important trend in information management. Image in particular, has been gaining popularity as an alternative, and sometimes more viable, option for information storage. While this presents a wealth of information, it also creates a great problem in retrieving appropriate and relevant information during searching. This has resulted in an enormous growth of interest, and much active research, into the extraction of relevant information from non text-based databases. In particular,content-based image retrieval (CBIR) systems have been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture, and shape or the semantic meaning of the images. To enhance the retrieval speed, most CBIR systems pre-process the images stored in the database. This is because feature extraction algorithms are often computationally expensive. If images are to be retrieved from the World-Wide-Web (WWW), the raw images have to be downloaded and processed in real time. In this case, the feature extraction speed becomes crucial. Ideally, systems should only use those feature extraction algorithms that are most suited for analysing the visual features that capture the common relationship between the images in hand. In this thesis, a statistical discriminant analysis based feature selection framework is proposed. Such a framework is able to select the most appropriate visual feature extraction algorithms by using relevance feedback only on the user labelled samples. The idea is that a smaller image sample group is used to analyse the appropriateness of each visual feature, and only the selected features will be used for image comparison and ranking. As the number of features is less, an improvement in the speed of retrieval is achieved. From experimental results, it is found that the retrieval accuracy for small sample data has also improved. Intelligent E-Business has been used as a case study in this thesis to demonstrate the potential of the framework in the application of image retrieval system. In addition, an inter-query framework has been proposed in this thesis. This framework is also based on the statistical discriminant analysis technique. A common approach in inter-query for a CBIR system is to apply the term-document approach. This is done by treating each image’s name or address as a term, and the query session as a document. However, scalability becomes an issue with this technique as the number of stored queries increases. Moreover, this approach is not appropriate for a dynamic image database environment. In this thesis, the proposed inter-query framework uses a cluster approach to capture the visual properties common to the previously stored queries. Thus, it is not necessary to “memorise” the name or address of the images. In order to manage the size of the user’s profile, the proposed framework also introduces a merging approach to combine clusters that are close-by and similar in their characteristics. Experiments have shown that the proposed framework has outperformed the short term learning approach. It also has the advantage that it eliminates the burden of the complex database maintenance strategies required in the term-document approach commonly needed by the interquery learning framework. Lastly, the proposed inter-query learning framework has been further extended by the incorporation of a new semantic structure. The semantic structure is used to connect the previous queries both visually and semantically. This structure provides the system with the ability to retrieve images that are semantically similar and yet visually different. To do this, an active learning strategy has been incorporated for exploring the structure. Experiments have again shown that the proposed new framework has outperformed the previous framework.

Thesis submitted in fulfillment of the requirements for the degree Master of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology / Small satellites, such as CubeSats are mainly utilized for space and earth imaging missions. Imaging CubeSats are equipped with high resolution cameras for the capturing of digital images, as well as mass storage devices for storing the images. The captured images are transmitted to the ground station and subsequently stored in a database. The main problem with stored images in a large image database, identified by researchers and developers within the last number of years, is the retrieval of precise, clear images and overcoming the semantic gap. The semantic gap relates to the lack of correlation between the semantic categories the user requires and the low level features that a content-based image retrieval system offers. Clear images are needed to be usable for applications such as mapping, disaster monitoring and town planning. The main objective of this thesis is the design and development of an image classification, storage and retrieval system for a CubeSat. This system enables efficient classification, storing and retrieval of images that are received on a daily basis from an in-orbit CubeSat. In order to propose such a system, a specific research methodology was chosen and adopted. This entails extensive literature reviews on image classification techniques and image feature extraction techniques, to extract content embedded within an image, and include studies on image database systems, data mining techniques and image retrieval techniques. The literature study led to a requirement analysis followed by the analyses of software development models in order to design the system. The proposed design entails classifying images using content embedded in the image and also extracting image metadata such as date and time. Specific features extraction techniques are needed to extract required content and metadata. In order to achieve extraction of information embedded in the image, colour feature (colour histogram), shape feature (Mathematical Morphology) and texture feature (GLCM) techniques were used. Other major contributions of this project include a graphical user interface which enables users to search for similar images against those stored in the database. An automatic image extractor algorithm was also designed to classify images according to date and time, and colour, texture and shape features extractor techniques were proposed. These ensured that when a user wishes to query the database, the shape objects, colour quantities and contrast contained in an image are extracted and compared to those stored in the database. Implementation and test results concluded that the designed system is able to categorize images automatically and at the same time provide efficient and accurate results. The features extracted for each image depend on colour, shape and texture methods. Optimal values were also incorporated in order to reduce retrieval times. The mathematical morphological technique was used to compute shape objects using erosion and dilation operators, and the co-occurrence matrix was used to compute the texture feature of the image.