A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System

Learning and Leveraging Context for Maritime Threat Analysis: Vessel Classification using Exemplar-SVM

Modern fleet security requires accurate threat analysis in real-time, which relies on a range of contextual information (e.g., vessel size, speed, heading, etc.). Rich contextualization may be possible using imaging systems if the images can be used to detect and classify maritime vessels and track their movements. In this work, the effectiveness of the ensemble of Exemplar-SVMs (E-SVM) object ...

CNN based music emotion classification

Music emotion recognition (MER) is usually regarded as a multi-label tagging task, and each segment of music can inspire specific emotion tags. Most researchers extract acoustic features from music and explore the relations between these features and their corresponding emotion tags. Considering the inconsistency of emotions inspired by the same music segment for human beings, seeking for the k...

Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features

Learning Classification taxonomies from a classification knowledge based system

Knowledge-based systems (KBS) are not necessarily based on well-defined ontologies. In particular it is possible to build KBS for classification problems, where there is little constraint on how classes are organised and a class is expressed by the expert as a free text conclusion to a rule. This paper investigates how relations between such ’classes’ may be discovered from existing knowledge b...

Imbalanced Malware Images Classification: a CNN based Approach

Deep convolutional neural networks (CNNs) can be applied to malware binary detection through images classification. The performance, however, is degraded due to the imbalance of malware families (classes). To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final layer of deep CNNs. The original softmax loss is weighted, and the weight va...