We explore a novel approach for human detection based on random color similarity feature (RCS) and random ferns classifier which is also known as semi-naive Bayesian classifier. In contrast to other existing features employed by human detection, color-based features are rarely used in vision-based human detection because of large intra-class variations. In this paper, we propose a novel color-b...

Zero-shot learning (ZSL) is to construct recognition models for unseen target classes that have no labeled samples for training. It utilizes the class attributes or semantic vectors as side information and transfers supervision information from related source classes with abundant labeled samples. Existing ZSL approaches adopt an intermediary embedding space to measure the similarity between a ...

This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As basi...

As a popular technique for swapping faces with someone else’s in images or videos through deep neural networks, deepfake causes serious threat to the security of multimedia content today. However, because counterfeit are usually compressed when propagating over Internet, and compression factor used is unknown, most existing detection models have poor robustness unknown factors. To solve this pr...

The aim of this paper is to introduce improvements made to a new type of classifier based on maximal fuzzy similarity [1]. Improvements are based on the use of generalized Łukasiewicz-structures and weight optimization. The main benefits of the classifier are its computational efficiency and its strong mathematical background. It is based on many-valued logic and it provides semantical informat...

In this paper we propose a new method for classifying uncertain data, modeled as interval-valued fuzzy sets. We develop the notion of an interval-valued prototype-based fuzzy classifier, with the idea of preserving full information including the uncertainty factor about data during the classification process. To this end, the classifier was based on the uncertainty-aware similarity measure, a n...

The new trend in sentiment classification is to use semantic features for representation of documents. We propose a semantic space based on WordNet senses for a supervised document-level sentiment classifier. Not only does this show a better performance for sentiment classification, it also opens opportunities for building a robust sentiment classifier. We examine the possibility of using simil...