Exploiting Web Images for Dataset Construction: A Domain Robust Approach

TL;DR

This paper introduces a novel framework for automatically constructing image datasets from web images that maintains domain robustness and reduces bias, using multi-instance learning and semantic expansion.

Contribution

The paper presents a new dataset construction method that improves domain adaptation by filtering irrelevant images through semantic expansion and multi-instance learning.

Findings

The constructed dataset shows strong domain robustness in classification tasks.

The method effectively filters noisy images, enhancing dataset quality.

Experiments demonstrate improved cross-dataset generalization.

Abstract

Labelled image datasets have played a critical role in high-level image understanding. However, the process of manual labelling is both time-consuming and labor intensive. To reduce the cost of manual labelling, there has been increased research interest in automatically constructing image datasets by exploiting web images. Datasets constructed by existing methods tend to have a weak domain adaptation ability, which is known as the "dataset bias problem". To address this issue, we present a novel image dataset construction framework that can be generalized well to unseen target domains. Specifically, the given queries are first expanded by searching the Google Books Ngrams Corpus to obtain a rich semantic description, from which the visually non-salient and less relevant expansions are filtered out. By treating each selected expansion as a "bag" and the retrieved images as "instances", image selection can be formulated as a multi-instance learning problem with constrained positive bags. We propose to solve the employed problems by the cutting-plane and concave-convex procedure (CCCP) algorithm. By using this approach, images from different distributions can be kept while noisy images are filtered out. To verify the effectiveness of our proposed approach, we build an image dataset with 20 categories. Extensive experiments on image classification, cross-dataset generalization, diversity comparison and object detection demonstrate the domain robustness of our dataset.