Material Recognition in the Wild with the Materials in Context Database (1412.0623v2)

Abstract: Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild. MINC is an order of magnitude larger than previous material databases, while being more diverse and well-sampled across its 23 categories. Using MINC, we train convolutional neural networks (CNNs) for two tasks: classifying materials from patches, and simultaneous material recognition and segmentation in full images. For patch-based classification on MINC we found that the best performing CNN architectures can achieve 85.2% mean class accuracy. We convert these trained CNN classifiers into an efficient fully convolutional framework combined with a fully connected conditional random field (CRF) to predict the material at every pixel in an image, achieving 73.1% mean class accuracy. Our experiments demonstrate that having a large, well-sampled dataset such as MINC is crucial for real-world material recognition and segmentation.

• The paper introduces the MINC dataset, featuring over three million labeled samples across 23 material categories to enhance real-world material recognition.
• The paper develops deep CNN models for patch-based classification and full-scene segmentation, achieving mean class accuracies of 85.2% and 73.1% respectively.
• The paper demonstrates that using a balanced, large-scale dataset and refined network architectures significantly improves segmentation performance and boundary precision.

Material Recognition in the Wild with the Materials in Context Database

The paper "Material Recognition in the Wild with the Materials in Context Database" introduces a comprehensive approach to tackling the complex challenge of material recognition in real-world images. The authors focus on two primary contributions: the creation of a large-scale dataset named the Materials in Context Database (MINC), and the development of robust deep learning models for material recognition and segmentation.

Challenges and Dataset Creation

Material recognition in real-world contexts is inherently challenging due to variations in texture, geometry, lighting, and environmental clutter. Existing datasets such as the Flickr Material Database (FMD) lack the scale and diversity necessary for effective deep learning applications. Thus, the authors have compiled MINC, a dataset that is an order of magnitude larger, encompassing 23 distinct material categories with over three million labeled samples.

MINC consolidates data from both Flickr and Houzz images, ensuring diverse and well-sampled categories. The dataset is structured to include click-based labels, obtained through a streamlined, three-stage Amazon Mechanical Turk (AMT) pipeline, allowing efficient scaling while maintaining accuracy.

Methodology

The research leverages convolutional neural networks (CNNs) tailored to two tasks: patch-based material classification and full-scene segmentation. The authors trained CNNs using MINC to predict material labels from image patches, achieving an impressive 85.2% mean class accuracy with GoogLeNet architecture.

For full-scene material segmentation, the trained CNNs were converted into fully convolutional networks, enabling dense material prediction across entire images. By incorporating a fully connected conditional random field (CRF), the system enhances boundary precision, achieving a mean class accuracy of 73.1%.

Experimental Insights

Several experiments were conducted to evaluate the impact of network architecture, patch scale, and data size. Adjustments in CNN architectures, such as using GoogLeNet without average pooling, demonstrated significant improvements. Furthermore, the paper underscores the importance of dataset size and balance, revealing that larger, balanced datasets enhance the performance of deep learning models.

The results indicate that although clicks provide a cost-effective way to train CNNs, polygons are preferable for training CRFs due to their ability to improve boundary definitions. The findings also demonstrate that training on balanced datasets significantly outperforms training on imbalanced ones, while the ability to generalize from the MINC dataset indicates substantial advantages over existing datasets like FMD.

Implications and Future Directions

The introduction of MINC has practical and theoretical implications. It opens avenues for applications in robotics, image editing, and autonomous systems where context-aware material recognition is crucial. The dataset provides a foundation for advancing material recognition techniques, facilitating further exploration in joint material-object recognition tasks, and promoting the integration of attributes.

Future research could focus on expanding category diversity and improving cost-effectiveness in data annotation. Additionally, exploring new architectures or attribute-based learning may push the boundaries of current material recognition capabilities.

In conclusion, this paper represents a substantial step forward in material recognition, providing a robust dataset and methodology that serve both current demands and future explorations in the field. The open availability of MINC will undoubtedly foster further innovation and research collaboration.