[Paper Review] Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization

Rishabh Iyer, Pratik Dubal+4 more|arXiv (Cornell University)|Sep 24, 2018

Image Retrieval and Classification Techniques4 citations

TL;DR

Vis-DSS is an open-source toolkit for visual data selection and summarization that leverages submodular function optimization to enable efficient, scalable summarization of images, videos, and training data. It achieves state-of-the-art performance across tasks like video summarization, image collection summarization, and diversified active learning, with significant speedups via memoization techniques.

ABSTRACT

With increasing amounts of visual data being created in the form of videos and images, visual data selection and summarization are becoming ever increasing problems. We present Vis-DSS, an open-source toolkit for Visual Data Selection and Summarization. Vis-DSS implements a framework of models for summarization and data subset selection using submodular functions, which are becoming increasingly popular today for these problems. We present several classes of models, capturing notions of diversity, coverage, representation and importance, along with optimization/inference and learning algorithms. Vis-DSS is the first open source toolkit for several Data selection and summarization tasks including Image Collection Summarization, Video Summarization, Training Data selection for Classification and Diversified Active Learning. We demonstrate state-of-the art performance on all these tasks, and also show how we can scale to large problems. Vis-DSS allows easy integration for applications to be built on it, also can serve as a general skeleton that can be extended to several use cases, including video and image sharing platforms for creating GIFs, image montage creation, or as a component to surveillance systems and we demonstrate this by providing a graphical user-interface (GUI) desktop app built over Qt framework. Vis-DSS is available at https://github.com/rishabhk108/vis-dss

Motivation & Objective

• To address the growing challenge of managing massive volumes of redundant visual data from sources like drones, dash-cams, and smartphones.
• To reduce human labeling costs in machine learning by enabling effective data subset selection for training vision models.
• To unify diverse visual summarization tasks—image collection, video, training data selection, and active learning—under a single submodular optimization framework.
• To provide a scalable, extensible, and open-source software toolkit that supports real-world applications such as GIF creation, surveillance, and image montage generation.
• To demonstrate computational efficiency and state-of-the-art performance through extensive benchmarking on real-world datasets.

Proposed method

• The framework uses submodular functions to model diversity, coverage, representation, and importance in data summarization.
• It employs a modular design with pre-processing pipelines to extract visual features, scene information, object detection, face detection, and color data.
• Optimization is accelerated via memoization, reducing computation time by orders of magnitude compared to naive oracle-based evaluation.
• The system supports multiple summarization paradigms: query-based, entity-based (e.g., faces, objects), and importance-based summarization using learned classifiers.
• A GUI desktop application built on the Qt framework enables user interaction and real-time summarization of visual data.
• The toolkit supports four core applications: image collection summarization, video summarization, training data subset selection, and diversified active learning.

Experimental results

Research questions

• RQ1 How can submodular optimization be effectively applied to unify diverse visual summarization tasks such as video summarization and training data selection?
• RQ2 What are the relative performance trade-offs between different submodular models—diversity, coverage, representation, and importance—in various summarization scenarios?
• RQ3 Can memoization techniques significantly improve the scalability of submodular function evaluation for large-scale visual data, such as two-hour videos?
• RQ4 How does the framework perform in real-world applications like entity-based summarization (e.g., faces, objects) and query-based summarization?
• RQ5 To what extent can the toolkit be extended and integrated into practical systems like surveillance platforms or content-sharing applications?

Key findings

• The diversity model outperformed others in query-based summarization, particularly in handling over-represented scenes, due to its ability to select varied frames.
• The representation model achieved the best quantitative results in entity summarization (e.g., faces and objects), effectively skipping false positives and selecting representative instances.
• Memoization reduced summarization time from hundreds of seconds to under one second for a two-hour video, with gains up to 270x for certain functions.
• The framework achieved state-of-the-art performance across all evaluated tasks, including image collection summarization, video summarization, and training data subset selection.
• The implementation outperformed prior works like Gygli et al. [8] and SFO [18], with speedups of up to 150x on average for key functions such as Feature-Based and Set Coverage.
• The GUI desktop application successfully demonstrated real-time summarization, validating the toolkit’s usability and extensibility for end-user applications.