Novel CBIR System Based on Ripplet Transform Using Interactive Neuro-Fuzzy Technique

Content Based Image Retrieval (CBIR) system is an emerging research area in effective digital data management and retrieval paradigm. In this article, a novel CBIR system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result and to reduce the computational complexity, the proposed scheme utilizes a Neural Network (NN) based classifier for image pre-classification, similarity matching using Manhattan distance measure and relevance feedback mechanism (RFM) using fuzzy entropy based feature evaluation technique. Extensive experiments were carried out to evaluate the effectiveness of the proposed technique. The performance of the proposed CBIR system is evaluated using a 2 £ 5-fold cross validation followed by a statistical analysis. The experimental results suggest that the proposed system based on RT, performs better than many existing CBIR schemes based on other transforms, and the difference is statistically significant

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making

Intraclass and interclass ambiguities (fuzziness) in feature evaluation

The terms Index of fuzziness, Entropy and π-ness which give measures of fuzziness (ambiguity) in a set are used here to define an Index of Feature Evaluation in pattern recognition problems in terms of intraclass and interclass ambiguity. The index is seen to possess a lower value for the feature having more importance in characterising a class. The algorithm has been implemented on a speech recognition problem

Rule-based semantic sensing platform for activity monitoring

Sensors are playing an increasingly important role in our lives, and for these devices to perform to their maximum potential, they need to work together. A single device can provide a single service or a fixed set of services but, when combined with other sensors, different classes of applications become implementable. The vital criterion for this to happen is the ability to bring information from all sensors together, so that all measured physical phenomena can contribute to the solution. Mediation between applications and physical sensors is the responsibility of sensor network middleware (SNM). Rapid growth in the kinds of sensors and applications for sensors/sensor systems, and the consequent importance of sensor network middleware has raised the need to relatively rapidly build engineering applications from those components. A number of SNM exist, each of which attempts to solve the sensor integration problem in a different way. These solutions, based on their ‘closeness’ either to sensors or to applications, can be classified as low-level and high-level. Low-level SNM tends not to focus on making application development easy, while high-level SNM tends to be ‘locked-in’ to a particular set of sensors. We propose a SNM suitable for the task of activity monitoring founded on rules and events, integrated through a semantic event model. The proposed solution is intended to be open at the bottom – to new sensor types; and open at the top – to new applications/user requirements. We show evidence for the effectiveness of this approach in the context of two pilot studies in rehabilitation monitoring – in both hospital and home environment. Moreover, we demonstrate how the semantic event model and rule-based approach promotes verifiability and the ability to validate the system with domain experts