Semantic Detection of Adult Image Using Semantic Features

Abstract

Recently, in the fields of internet and social networking, the classification and filtering of naked images has been receiving a significant amount of attention. In this paper, we propose a novel naked image classification which can make effective use of semantic features of a naked image. In addition, a novel measurement, termed accumulated distance ratio (ADR), is proposed in order to systematically analyze the effect of semantic features on improving classification performance, compared to the approach relying on low-level visual features. Extensive experiments have been carried out to assess the effectiveness of semantic features in naked image classification with realistic and challenging data set. The experimental result of the proposed approach using semantic features, for challenging data set, shows improvement up to 14% than the approach using low-level visual feature. Further, the proposed ADR measure has proven to be useful measure for analyzing the effect of semantic features for naked image classification.