Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks

Image processing is a rapidly growing research area of computer science and remains as a challenging problem within the computer vision fields. For the classification of flower images, the problem is mainly due to the huge similarities in terms of colour and texture. The appearance of the image itself such as variation of lights due to different lighting condition, shadow effect on the object’s surface, size, shape, rotation and position, background clutter, states of blooming or budding may affect the utilized classification techniques. This study aims to develop a classification model for Malaysian blooming flowers using neural network with the back propagation algorithms. The flower image is extracted through Region of Interest (ROI) in which texture and colour are emphasized in this study. In this research, a total of 960 images were extracted from 16 types of flowers. Each ROI was represented by three colour attributes (Hue, Saturation, and Value) and four textures attribute (Contrast, Correlation, Energy and Homogeneity). In training and testing phases, experiments were carried out to observe the classification performance of Neural Networks with duplication of difficult pattern to learn (referred to as DOUBLE) as this could possibly explain as to why some flower images were difficult to learn by classifiers. Results show that the overall performance of Neural Network with DOUBLE is 96.3% while actual data set is 68.3%, and the accuracy obtained from Logistic Regression with actual data set is 60.5%. The Decision Tree classification results indicate that the highest performance obtained by Chi-Squared Automatic Interaction Detection(CHAID) and Exhaustive CHAID (EX-CHAID) is merely 42% with DOUBLE. The findings from this study indicate that Neural Network with DOUBLE data set produces highest performance compared to Logistic Regression and Decision Tree. Therefore, NN has been potential in building Malaysian blooming flower model. Future studies can be focused on increasing the sample size and ROI thus may lead to a higher percentage of accuracy. Nevertheless, the developed flower model can be used as part of the Malaysian Blooming Flower recognition system in the future where the colours and texture are needed in the flower identification process

The relaxation method for learning in artificial neural networks

A new mathematical approach for deriving learning algorithms for various neural network models including the Hopfield model, Bidirectional Associative Memory, Dynamic Heteroassociative Neural Memory, and Radial Basis Function Networks is presented. The mathematical approach is based on the relaxation method for solving systems of linear inequalities. The newly developed learning algorithms are fast and they guarantee convergence to a solution in a finite number of steps. The new algorithms are highly insensitive to choice of parameters and the initial set of weights. They also exhibit high scalability on binary random patterns. Rigorous mathematical foundations for the new algorithms and their simulation studies are included

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)

3-D Content-Based Retrieval and Classification with Applications to Museum Data

There is an increasing number of multimedia collections arising in areas once only the domain of text and 2-D images. Richer types of multimedia such as audio, video and 3-D objects are becoming more and more common place. However, current retrieval techniques in these areas are not as sophisticated as textual and 2-D image techniques and in many cases rely upon textual searching through associated keywords. This thesis is concerned with the retrieval of 3-D objects and with the application of these techniques to the problem of 3-D object annotation. The majority of the work in this thesis has been driven by the European project, SCULPTEUR. This thesis provides an in-depth analysis of a range of 3-D shape descriptors for their suitability for general purpose and specific retrieval tasks using a publicly available data set, the Princeton Shape Benchmark, and using real world museum objects evaluated using a variety of performance metrics. This thesis also investigates the use of 3-D shape descriptors as inputs to popular classification algorithms and a novel classifier agent for use with the SCULPTEUR system is designed and developed and its performance analysed. Several techniques are investigated to improve individual classifier performance. One set of techniques combines several classifiers whereas the other set of techniques aim to find the optimal training parameters for a classifier. The final chapter of this thesis explores a possible application of these techniques to the problem of 3-D object annotation

Learning on relevance feedback in content-based image retrieval.

Hoi, Chu-Hong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 89-103).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Content-based Image Retrieval --- p.1Chapter 1.2 --- Relevance Feedback --- p.3Chapter 1.3 --- Contributions --- p.4Chapter 1.4 --- Organization of This Work --- p.6Chapter 2 --- Background --- p.8Chapter 2.1 --- Relevance Feedback --- p.8Chapter 2.1.1 --- Heuristic Weighting Methods --- p.9Chapter 2.1.2 --- Optimization Formulations --- p.10Chapter 2.1.3 --- Various Machine Learning Techniques --- p.11Chapter 2.2 --- Support Vector Machines --- p.12Chapter 2.2.1 --- Setting of the Learning Problem --- p.12Chapter 2.2.2 --- Optimal Separating Hyperplane --- p.13Chapter 2.2.3 --- Soft-Margin Support Vector Machine --- p.15Chapter 2.2.4 --- One-Class Support Vector Machine --- p.16Chapter 3 --- Relevance Feedback with Biased SVM --- p.18Chapter 3.1 --- Introduction --- p.18Chapter 3.2 --- Biased Support Vector Machine --- p.19Chapter 3.3 --- Relevance Feedback Using Biased SVM --- p.22Chapter 3.3.1 --- Advantages of BSVM in Relevance Feedback --- p.22Chapter 3.3.2 --- Relevance Feedback Algorithm by BSVM --- p.23Chapter 3.4 --- Experiments --- p.24Chapter 3.4.1 --- Datasets --- p.24Chapter 3.4.2 --- Image Representation --- p.25Chapter 3.4.3 --- Experimental Results --- p.26Chapter 3.5 --- Discussions --- p.29Chapter 3.6 --- Summary --- p.30Chapter 4 --- Optimizing Learning with SVM Constraint --- p.31Chapter 4.1 --- Introduction --- p.31Chapter 4.2 --- Related Work and Motivation --- p.33Chapter 4.3 --- Optimizing Learning with SVM Constraint --- p.35Chapter 4.3.1 --- Problem Formulation and Notations --- p.35Chapter 4.3.2 --- Learning boundaries with SVM --- p.35Chapter 4.3.3 --- OPL for the Optimal Distance Function --- p.38Chapter 4.3.4 --- Overall Similarity Measure with OPL and SVM --- p.40Chapter 4.4 --- Experiments --- p.41Chapter 4.4.1 --- Datasets --- p.41Chapter 4.4.2 --- Image Representation --- p.42Chapter 4.4.3 --- Performance Evaluation --- p.43Chapter 4.4.4 --- Complexity and Time Cost Evaluation --- p.45Chapter 4.5 --- Discussions --- p.47Chapter 4.6 --- Summary --- p.48Chapter 5 --- Group-based Relevance Feedback --- p.49Chapter 5.1 --- Introduction --- p.49Chapter 5.2 --- SVM Ensembles --- p.50Chapter 5.3 --- Group-based Relevance Feedback Using SVM Ensembles --- p.51Chapter 5.3.1 --- (x+l)-class Assumption --- p.51Chapter 5.3.2 --- Proposed Architecture --- p.52Chapter 5.3.3 --- Strategy for SVM Combination and Group Ag- gregation --- p.52Chapter 5.4 --- Experiments --- p.54Chapter 5.4.1 --- Experimental Implementation --- p.54Chapter 5.4.2 --- Performance Evaluation --- p.55Chapter 5.5 --- Discussions --- p.56Chapter 5.6 --- Summary --- p.57Chapter 6 --- Log-based Relevance Feedback --- p.58Chapter 6.1 --- Introduction --- p.58Chapter 6.2 --- Related Work and Motivation --- p.60Chapter 6.3 --- Log-based Relevance Feedback Using SLSVM --- p.61Chapter 6.3.1 --- Problem Statement --- p.61Chapter 6.3.2 --- Soft Label Support Vector Machine --- p.62Chapter 6.3.3 --- LRF Algorithm by SLSVM --- p.64Chapter 6.4 --- Experimental Results --- p.66Chapter 6.4.1 --- Datasets --- p.66Chapter 6.4.2 --- Image Representation --- p.66Chapter 6.4.3 --- Experimental Setup --- p.67Chapter 6.4.4 --- Performance Comparison --- p.68Chapter 6.5 --- Discussions --- p.73Chapter 6.6 --- Summary --- p.75Chapter 7 --- Application: Web Image Learning --- p.76Chapter 7.1 --- Introduction --- p.76Chapter 7.2 --- A Learning Scheme for Searching Semantic Concepts --- p.77Chapter 7.2.1 --- Searching and Clustering Web Images --- p.78Chapter 7.2.2 --- Learning Semantic Concepts with Relevance Feed- back --- p.73Chapter 7.3 --- Experimental Results --- p.79Chapter 7.3.1 --- Dataset and Features --- p.79Chapter 7.3.2 --- Performance Evaluation --- p.80Chapter 7.4 --- Discussions --- p.82Chapter 7.5 --- Summary --- p.82Chapter 8 --- Conclusions and Future Work --- p.84Chapter 8.1 --- Conclusions --- p.84Chapter 8.2 --- Future Work --- p.85Chapter A --- List of Publications --- p.87Bibliography --- p.10

An investigation into adaptive power reduction techniques for neural hardware

In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing field [1] and the present thrust of the semiconductor industry towards lowpower SOCs for mobile devices [2], the power consumption of ANN hardware has become a very important implementation issue. Adaptability is a powerful and useful feature of neural networks. All current approaches for low-power ANN hardware techniques are ‘non-adaptive’ with respect to the power consumption of the network (i.e. power-reduction is not an objective of the adaptation/learning process). In the research work presented in this thesis, investigations on possible adaptive power reduction techniques have been carried out, which attempt to exploit the adaptability of neural networks in order to reduce the power consumption. Three separate approaches for such adaptive power reduction are proposed: adaptation of size, adaptation of network weights and adaptation of calculation precision. Initial case studies exhibit promising results with significantpower reduction

Statistical Learning Approaches to Information Filtering

Enabling computer systems to understand human thinking or behaviors has ever been an exciting challenge to computer scientists. In recent years one such a topic, information filtering, emerges to help users find desired information items (e.g.~movies, books, news) from large amount of available data, and has become crucial in many applications, like product recommendation, image retrieval, spam email filtering, news filtering, and web navigation etc.. An information filtering system must be able to understand users' information needs. Existing approaches either infer a user's profile by exploring his/her connections to other users, i.e.~collaborative filtering (CF), or analyzing the content descriptions of liked or disliked examples annotated by the user, ~i.e.~content-based filtering (CBF). Those methods work well to some extent, but are facing difficulties due to lack of insights into the problem. This thesis intensively studies a wide scope of information filtering technologies. Novel and principled machine learning methods are proposed to model users' information needs. The work demonstrates that the uncertainty of user profiles and the connections between them can be effectively modelled by using probability theory and Bayes rule. As one major contribution of this thesis, the work clarifies the ``structure'' of information filtering and gives rise to principled solutions. In summary, the work of this thesis mainly covers the following three aspects: Collaborative filtering: We develop a probabilistic model for memory-based collaborative filtering (PMCF), which has clear links with classical memory-based CF. Various heuristics to improve memory-based CF have been proposed in the literature. In contrast, extensions based on PMCF can be made in a principled probabilistic way. With PMCF, we describe a CF paradigm that involves interactions with users, instead of passively receiving data from users in conventional CF, and actively chooses the most informative patterns to learn, thereby greatly reduce user efforts and computational costs. Content-based filtering: One major problem for CBF is the deficiency and high dimensionality of content-descriptive features. Information items (e.g.~images or articles) are typically described by high-dimensional features with mixed types of attributes, that seem to be developed independently but intrinsically related. We derive a generalized principle component analysis to merge high-dimensional and heterogenous content features into a low-dimensional continuous latent space. The derived features brings great conveniences to CBF, because most existing algorithms easily cope with low-dimensional and continuous data, and more importantly, the extracted data highlight the intrinsic semantics of original content features. Hybrid filtering: How to combine CF and CBF in an ``smart'' way remains one of the most challenging problems in information filtering. Little principled work exists so far. This thesis reveals that people's information needs can be naturally modelled with a hierarchical Bayesian thinking, where each individual's data are generated based on his/her own profile model, which itself is a sample from a common distribution of the population of user profiles. Users are thus connected to each other via this common distribution. Due to the complexity of such a distribution in real-world applications, usually applied parametric models are too restrictive, and we thus introduce a nonparametric hierarchical Bayesian model using Dirichlet process. We derive effective and efficient algorithms to learn the described model. In particular, the finally achieved hybrid filtering methods are surprisingly simple and intuitively understandable, offering clear insights to previous work on pure CF, pure CBF, and hybrid filtering