Content-based image retrieval of museum images

Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections

Monitoring and Detection of Hotspots using Satellite Images

Nowadays, the usage of optical remote sensing NOAA-AVHRR satellite data is getting familiar as it is known can save cost in order to capture a wide coverage of ground image. The captured images are meaningful after several processes done over it to produce hotspot detection. Developing a specific database to store information of Hotspots (LAC images) would make datamanagement and archiving purpose in more efficient and systematic way. Real-time data gathered are monitored countries such as Malaysia, Thailand, Singapore, Indonesia and Brunei within the region of NOAA Satellite coverage area. PostGIS, PostgreSQL, Mapserver and Autodesk MapGuide Studio software are to be studied as a guide to develop a system with simple database using object-relational database management system to store raster and vector images. This paper describes a solution for efficient handling of large raster image data sets in a standard object-relational database management system. By means of adequate indexing, retrieval techniques and multi resolution cell indexing (Quad-Tree) can be achieved using a standard DBMS, even for very large satellite images. Single image will be divided equally into 64 small squares (3 levels of image hierarchy - each level has 4 sub images of the higher image). Partial information of Daily Haze report (processed Hotspot on image map) produces by NREB can be viewed using web-based application. The final product of this project is a web-based application for displaying Hotspots on maps (combination of raster and vector images) with the ability to search record from database and functions to zoom in or zoom out the map. The objective of this paper is also to show the way satellite images and descriptive information are combined and amalgamated to form an Internet or Intranet application

Fractal transforms and feature invariance

In this paper, fractal transforms are employed with the aim of image recognition. It is known that such transforms are highly sensitive to distortions like a small shift of an image. However, by using features based on statistics kept during the actual decomposition we can derive features from fractal transforms, which are invariant to perturbations like rotation, translation, folding or contrast scaling. Further, we introduce a feature invariance measure, which reveals the degree of invariance of a feature with respect to a database. The features and the way their invariance is measured, appear well suited for the application to images of textures

Giving eyes to ICT!, or How does a computer recognize a cow?

Het door Schouten en andere onderzoekers op het CWI ontwikkelde systeem berust op het beschrijven van beelden met behulp van fractale meetkunde. De menselijke waarneming blijkt mede daardoor zo efficiënt omdat zij sterk werkt met gelijkenissen. Het ligt dus voor de hand het te zoeken in wiskundige methoden die dat ook doen. Schouten heeft daarom beeldcodering met behulp van 'fractals' onderzocht. Fractals zijn zelfgelijkende meetkundige figuren, opgebouwd door herhaalde transformatie (iteratie) van een eenvoudig basispatroon, dat zich daardoor op steeds kleinere schalen vertakt. Op elk niveau van detaillering lijkt een fractal op zichzelf (Droste-effect). Met fractals kan men vrij eenvoudig bedrieglijk echte natuurvoorstellingen maken. Fractale beeldcodering gaat ervan uit dat het omgekeerde ook geldt: een beeld effectief opslaan in de vorm van de basispatronen van een klein aantal fractals, samen met het voorschrift hoe het oorspronkelijke beeld daaruit te reconstrueren. Het op het CWI in samenwerking met onderzoekers uit Leuven ontwikkelde systeem is mede gebaseerd op deze methode. ISBN 906196502

A Novel Two-Stage Approach For Automatic Detection of Brain Tumor

Brain tumor is one of the most life-threatening diseases, and it is the most common type of cancer that occurs among those in the age group belonging to 0-19. It is also a major cause of cancer-related deaths in children (males and females) under age 20 hence its detection should be fast and accurate. Manual detection of brain tumors using MRI scan images is effective but time-consuming. Many automation techniques and algorithms for detection of brain tumors are being proposed recently. In this paper, we propose an integrated two-step approach combining modified K-means clustering algorithm and Hierarchical Centroid Shape Descriptor (HCSD). The images are clustered using modified K-means based on pixel intensity, and then HCSD helps to select those having a specific shape thus making this approach more effective and reliable. Simulation of the proposed work is done in MATLAB R2013a. Tests are carried out on T1 weighted MRI scan images

6 Access Methods and Query Processing Techniques

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som

Parallelizing support vector machines for scalable image annotation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. In this thesis distributed computing paradigms have been investigated to speed up SVM training, by partitioning a large training dataset into small data chunks and process each chunk in parallel utilizing the resources of a cluster of computers. A resource aware parallel SVM algorithm is introduced for large scale image annotation in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of the algorithm in heterogeneous computing environments. SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. A resource aware parallel multiclass SVM algorithm for large scale image annotation in parallel using a cluster of computers is introduced. The combination of classifiers leads to substantial reduction of classification error in a wide range of applications. Among them SVM ensembles with bagging is shown to outperform a single SVM in terms of classification accuracy. However, SVM ensembles training are notably a computationally intensive process especially when the number replicated samples based on bootstrapping is large. A distributed SVM ensemble algorithm for image annotation is introduced which re-samples the training data based on bootstrapping and training SVM on each sample in parallel using a cluster of computers. The above algorithms are evaluated in both experimental and simulation environments showing that the distributed SVM algorithm, distributed multiclass SVM algorithm, and distributed SVM ensemble algorithm, reduces the training time significantly while maintaining a high level of accuracy in classifications