In content-based image retrieval (CBIR) system, one approach of image representation is to employ combination of low-level visual features cascaded together into a flat vector. While this presents more descriptive information, it however poses serious challenges in terms of high dimensionality and high computational cost of feature extraction algorithms to deployment of CBIR on platforms (devices) with limited computational and storage resources. Hence, in this work a feature dimensionality reduction technique based on principal component analysis (PCA) is implemented. Each image in a database is indexed using 174-dimensional feature vector comprising of 54-dimensional colour moments (CM54), 32-bin HSV-histogram (HIST32), 48-dimensional gabor wavelet (GW48) and 40-dimensional wavelet moments (MW40). The PCA scheme was incorporated into a CBIR system that utilized the entire feature vector space. The k-largest eigenvalues that yielded a not more than 5% degradation in mean precision were retained for dimensionality reduction. Three image databases (DB10, DB20 and DB100) were used for testing. The result obtained showed that with 80% reduction in feature dimensions, tolerable loss of 3.45, 4.39 and 7.40% in mean precision value were achieved on DB10, DB20 and DB100.
Image segmentation still remains an important task in image processing and analysis. Sequel to any segmentation process, preprocessing activities carried out on the images have a great effect on the accuracy of the segmentation task. This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms. Subsequently, brain tissue extraction tool was employed in extracting non-brain tissues from the brain image. Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (FCM) segmentation algorithms were employed for segmenting brain MRI images acquired from four different MRI databases into their White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) constituents. Evaluation metrics such as cluster validity functions using partition coefficients and partition entropy; area error metrics such as false positive, true positive, true negative and false negative (FN); similarity index, sensitivity and specificity were used to evaluate the performance of both techniques. A comparative analysis of the experimental results revealed that in most instances, FKM segmentation technique is preferable to FCM segmentation technique for brain MRI segmentation task.
Simultaneous radial distribution network reconfiguration (RDNR) and shunt capacitor allocation (SCA) is one of the compensation techniques that are used for getting an improved radial structure with reduced real power loss and enhanced voltage stability. This study presents a novel adaptive particle swarm optimisation (APSO) technique for the simultaneous RDNR and SCA, which is a complex and nonlinear optimisation problem. Unlike the conventional particle swarm optimization (PSO) technique in which an initial population of particles is randomly generated, the fundamental loop concept is used to populate the search space of APSO with the candidate branches for each tie switch (open branch) in the loop. The candidate branches are preselected with the graph theory. This is done to mitigate infeasible configurations in the optimization process and also to ensure that the conditions for radiality of the network are satisfied. The effectiveness of the proposed APSO technique for simultaneous RDNR and SCA is demonstrated on the standard IEEE 33-bus and Nigerian Ayepe 34-bus RDNs using six event cases. The efficacy of the proposed APSO technique is further validated with the comparison of the observed simulation results with the reported results of similar work implemented with established algorithms like improved binary particle swarm optimization (IBPSO), modified pollinated flower algorithm (MFPA) and mixed integer linear programming (MILP). The result of the comparative study reveals that the proposed APSO technique outperforms the selected algorithms in most of the considered event cases.
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