Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database

This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.Comment: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 24-26 June 201

Class specific feature selection for identity validation using dynamic signatures

Classification of the biometrics data for identity validation can be modeled as a single-class problem, where the identity is confirmed by comparing the biometrics of the unknown person with those of the claimed identity. However, current feature selection techniques do not differentiate between single-class and multi-class problems when determining the suitable feature set and select the feature-set that is suitable for representing or discriminating for all the available classes. This may not be the best representation of the biometrics data of an individual because different people may have differences in the most suitable features to represent their biometrical data. In this paper, a class-specific feature selection method has been proposed and experimentally validated using dynamic signatures. This method is based on the coefficient of variance within the feature set, where the features with smaller variance are selected and the ones with larger variance are rejected. The proposed technique was compared with the other feature selection methods, and the results show that a significant improvement in the classification accuracy, specificity and sensitivity was obtained when using class-specific feature selection

Image Classification Using Bag-of-Visual-Words Model

Recently, with the explosive growth of digital technologies, there has been a rapid proliferation of the size of image collection. The technique of supervised image clas sification has been widely applied in many domains in order to organize, search, and retrieve images. However, the traditional feature extraction approaches yield the poor classification accuracy. Therefore, the Bag-of-visual-words model, inspired by Bag-of Words model in document classification, was used to present images with the local descriptors for image classification, and also it performs well in some fields. This research provides the empirical evidence to prove that the BoVW model outperforms the traditional feature extraction approaches for both binary image clas sification and multi-class image classification. Furthermore, the research reveals that the size of the visual vocabulary during the process of building BoVW model impact on the accuracy results of image classification