Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification

TL;DR

This paper proposes a deep learning framework that combines scene text recognition and visual features to improve fine-grained image classification and retrieval, demonstrating significant performance gains on relevant datasets.

Contribution

It introduces a novel method integrating scene text and visual features into a trainable CNN for enhanced image classification and retrieval.

Findings

Significant accuracy improvement over visual-only methods.

Effective use of attention mechanism to relate text and images.

Enhanced retrieval performance in product search applications.

Abstract

Text in natural images contains rich semantics that are often highly relevant to objects or scene. In this paper, we focus on the problem of fully exploiting scene text for visual understanding. The main idea is combining word representations and deep visual features into a globally trainable deep convolutional neural network. First, the recognized words are obtained by a scene text reading system. Then, we combine the word embedding of the recognized words and the deep visual features into a single representation, which is optimized by a convolutional neural network for fine-grained image classification. In our framework, the attention mechanism is adopted to reveal the relevance between each recognized word and the given image, which further enhances the recognition performance. We have performed experiments on two datasets: Con-Text dataset and Drink Bottle dataset, that are proposed for fine-grained classification of business places and drink bottles, respectively. The experimental results consistently demonstrate that the proposed method combining textual and visual cues significantly outperforms classification with only visual representations. Moreover, we have shown that the learned representation improves the retrieval performance on the drink bottle images by a large margin, making it potentially useful in product search.