Multiple Objects Matting Dataset

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Project Overview:

Objective

Developing a rich dataset tailored for the task of matting multiple objects within diverse scenarios is essential. This dataset would be instrumental in enhancing technologies such as video editing software, augmented reality (AR) applications, and real-time visual effects production. Furthermore, such a dataset would support the refinement of algorithms, leading to more accurate and efficient object separation and background replacement.

Scope

Curate images showcasing multiple objects, both animate and inanimate, across various backgrounds and settings. Moreover, every image will possess high-quality alpha mattes for each object, thereby enabling precise foreground extraction.

Sources

Engaged in collaborative partnerships with photographers and film production houses, leading to a meticulously collected and successfully curated array of visual content.
Meticulously collected crowdsourced images from global contributors have ensured a wide variety of objects and backgrounds in the dataset.
Captured in controlled environments to include specific and challenging matting scenarios, such as fine hair against contrasting backgrounds, resulting in a successfully collected and thoughtfully curated visual dataset.
Public datasets have been carefully refined and expanded upon, contributing to a successfully collected and professionally curated set of resources.

Data Collection Metrics

Total Images: 35,000
Urban & Natural Landscapes: 15,000
Indoor Settings: 10,000
Controlled Environments: 7,000
Miscellaneous: 3,000

Annotation Process

Stages

Image Pre-processing: Standardization for resolution, clarity, and brightness.
Alpha Matte Generation: Subsequently, expert annotators will produce high-resolution alpha mattes for every distinct object in the images by leveraging cutting-edge matting tools.
Validation: Moreover, a dual-layered validation process involving both automated algorithms and human reviewers will be employed to ensure the integrity of the mattes.

Annotation Metrics

Total Alpha Mattes: 100,000+ (Given multiple objects per image)
Average Annotation Time per Image: 40 minutes (Due to the intricacy of creating detailed mattes for multiple objects)

Quality Assurance

Stages

Automated Checks: Proprietary matting algorithms evaluate the mattes for potential errors or artifacts.
Peer Review: Furthermore, a subset of images and their associated mattes undergo thorough peer scrutiny
Inter-annotator Agreement: Moreover, for Inter-annotator Agreement, complex images are assigned to multiple annotators, ensuring a unanimous understanding of matting nuances.

QA Metrics

Mattes Verified using Algorithms: 17,500 (50% of total images)
Peer-reviewed Mattes: 10,500 (30% of total images)
Inconsistencies Identified and Amended: 350 (1% of total images)

Conclusion

The Multiple Objects Matting Dataset represents a pioneering endeavor in the realm of digital media editing and augmented reality. By providing high-fidelity mattes for numerous objects within varied scenarios, it not only equips AI models to swiftly and accurately extract foreground elements but also paves the way for innovative visual projects and tools. Consequently, this dataset stands as a significant contribution to advancing technology, thereby enabling new possibilities in creative and technical applications.