CONTENT BASED IMAGE RETRIEVAL (CBIR) SYSTEM

Advancement in hardware and telecommunication technology has boosted up creation and distribution of digital visual content. However this rapid growth of visual content creations has not been matched by the simultaneous emergence of technologies to support efficient image analysis and retrieval. Although there are attempt to solve this problem by using meta-data text annotation but this approach are not practical when it come to the large number of data collection. This system used 7 different feature vectors that are focusing on 3 main low level feature groups (color, shape and texture). This system will use the image that the user feed and search the similar images in the database that had similar feature by considering the threshold value. One of the most important aspects in CBIR is to determine the correct threshold value. Setting the correct threshold value is important in CBIR because setting it too low will result in less image being retrieve that might exclude relevant data. Setting to high threshold value might result in irrelevant data to be retrieved and increase the search time for image retrieval. Result show that this project able to increase the image accuracy to average 70% by combining 7 different feature vector at correct threshold value. ii

The Consensus Clustering as a Contribution to Parental Recognition Problem Based on Hand Biometrics

The clustering analysis is a subject that has been interesting researchers from several areas, such as health (medical diagnosis, clustering of proteins and genes), marketing (market analysis and image segmentation), information management (clustering of web pages). The clustering algorithms are usually applied in Data Mining, allowing the identification of natural groups for a given data set. The use of different clustering methods for the same data set can produce different groups. So, several studies have been led to validate the resulting clusters. There has been an increasing interest on how to determine a consensus clustering that combines the different individual clusterings, reflecting the main structure in clusters inherent to each of them, as a perspective to get a higher quality clustering. As several techniques of consensus clustering have been researched, the present work focuses on problem of finding the best partition in the consensus clustering. We analyze the most referred techniques in literature, the consensus clustering techniques with different mechanisms to achieve the consensus, i.e.; Voting mechanisms; Co-association matrix; Mutual Information and hyper-graphs; and a multi-objective consensus clustering existing on literature. In this paper we discuss these approaches and a comparative study is presented, that considers a set of experiments using two-dimensional synthetic data sets with different characteristics, as number of clusters, their cardinality, shape, homogeneity and separability, and a real-world data set based on hand\u27s biometrics shape, in context of people parental recognition. With this data we intend to investigate the ability of the consensus clustering algorithms in correctly cluster a child and her/his parents. This has an enormous business potential leading to a great economic value, since that with this technology a website can match data, as hand\u27s photographs, and say if A and B are related somehow. We conclude that, in some cases, the multi-objective technique proved to outperform the other techniques, and unlike the other techniques, is little influenced by poor clustering even in situations like noise introduction and clusters with different homogeneity or overlapped. Furthermore, shows that can capture the performance of the best base clustering and still outperform it. Regarding to real data, no technique was capable of identifying a person\u27s mother/father. However, the research of distances between hands from a person and its father, mother, siblings, can retrieve the probability of that person being his/her familiar. This doesn\u27t enable the identification of relatives but instead, decreases the size of database for seeking the matches

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Digital image classification for Malaysian blooming flower

Digital image processing is a rapidly growing area of computer science since it was introduced and developed in the 1960’s. In the case of flower classification, image processing is a crucial step for computer-aided plant species identification.Colour of the flower plays very important role in image classification since it gives additional information in terms of segmentation and recognition. On the other hand,Texture can be used to facilitate image-based retrieval system normally and it is encoded by a number of descriptors, which represented by a set of statistical measures such as gray-level co-occurrence matrix (GLCM) and Law’s Order approach. This study addresses the application of NN and on image processing particularly for understanding flower image features.For predictive analysis, two techniques have been used namely, Neural Network (NN) and Logistic regression. The study shows that NN obtains the higher percentage of accuracy among two techniques.The MLP is trained by 1800 flower's dataset to classify 30 kinds of flower's type

Automatic Multilevel Feature Abstraction in Adaptable Machine Vision Systems

Vision is a complex task which can be accomplished with apparent ease by biological systems, but for which the design of artificial systems is difficult. Although machine vision systems can be successfully designed for a specific task, under certain conditions, they are likely to fail if circumstances change. This was the motivation for the research into ways in which systems can be self-designing and adaptable to new visual tasks. The research was conducted in three vital areas of concern for machine vision systems. The first area is finding a suitable architecture for forming an appropriate representation for the current task. The research investigated the application of Hypernetworks theory to building a multilevel, generally-applicable representation, through repeated application of a fundamental 'self-similarity' principle, that parts of objects assembled under a particular relation at one level, form whole objects at the next. Results show that this is potentially a powerful approach for autonomously generating an adaptable system-architecture suitable for multiple visual tasks. The second area is the autonomous extraction of suitable low-level features, which the research investigated through random generation of minimally-constrained pixel-configurations and algorithmic generation of homogeneous and heterogeneous polygons. The results suggest that, despite the simplicity of the features making them vulnerable to image transformations, these are promising approaches worth developing further. The third area is automatic feature selection. The research explored management of 'dimensionality' and of 'combinatorial explosion', as well as how to locate relevant features at multiple representation levels, in the context of 'emergence' of structure. Results indicate that this approach can find useful 'intermediate-level' constructs through analysis of the connectivity of the simplices representing objects at higher levels. The research concludes that the proposed novel approaches to tackling the above issues, in particular the application of hypernetworks to the formation of multilevel representations and the resulting emergence of higher-level structure, is fruitful