A Review on Deep Learning Approaches for Spectral Imaging

Deep learning algorithms have revolutionized the computer vision field in the last decade. They can reduce tedious feature engineering and have opened new possibilities of automated visual inspection. With deep learning techniques, the availability of large amounts of qualitative labeled data became more important than ever. The main share of computer vision research focuses on RGB images. With the advances in sensor technologies multi- and hyperspectral cameras have become more cost effective and accessible in recent years, allowing this imaging technology to be applied to new fields of application. This article gives an overview of approaches to apply deep learning techniques to multi- or hyperspectral data. Several state-of-the-art methods will be reviewed and problems and difficulties will be discussed. An overview of a selection of available datasets is presented. To give a broad and diverse insight, research from different fields of application are considered, namely the remote sensing domain, the agricultural domain and the food industry

Feature Selection for Big Visual Data: Overview and Challenges

International Conference Image Analysis and Recognition (ICIAR 2018, Póvoa de Varzim, Portugal

CEAI: CCM based Email Authorship Identification Model

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors' constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1, 2]