Content and Context Features for Scene Image Representation

TL;DR

This paper introduces novel content and context features for scene image classification, demonstrating that their fusion significantly enhances classification accuracy over existing methods.

Contribution

It proposes new multi-scale deep content features and web-based annotation context features, and shows their effective fusion improves scene classification.

Findings

Fusion of content and context features outperforms individual features.

Proposed features outperform existing features on benchmark datasets.

Significant accuracy improvement with feature fusion.

Abstract

Existing research in scene image classification has focused on either content features (e.g., visual information) or context features (e.g., annotations). As they capture different information about images which can be complementary and useful to discriminate images of different classes, we suppose the fusion of them will improve classification results. In this paper, we propose new techniques to compute content features and context features, and then fuse them together. For content features, we design multi-scale deep features based on background and foreground information in images. For context features, we use annotations of similar images available in the web to design a filter words (codebook). Our experiments in three widely used benchmark scene datasets using support vector machine classifier reveal that our proposed context and content features produce better results than existing context and content features, respectively. The fusion of the proposed two types of features significantly outperform numerous state-of-the-art features.