Book ChapterDOI
Searching in Video Collections Using Sketches and Sample Images – The Cineast System
Luca Rossetto,Ivan Giangreco,Silvan Heller,Claudiu Tanase,Heiko Schuldt +4 more
- pp 336-341
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TLDR
Cineast is presented, a content-based video retrieval engine which retrieves video sequences based on their visual content by using a multitude of low-level visual features in parallel.Abstract:
With the increasing omnipresence of video recording devices and the resulting abundance of digital video, finding a particular video sequence in ever-growing collections is more and more becoming a major challenge. Existing approaches to retrieve videos based on their content usually require prior knowledge about the origin and context of a particular video to work properly. Therefore, most state of the art video platforms still rely on text-based retrieval techniques to find desired sequences. In this paper, we present Cineast, a content-based video retrieval engine which retrieves video sequences based on their visual content. It supports Query-by-Example as well as Query-by-Sketch by using a multitude of low-level visual features in parallel. Cineast uses a collection of 200 videos from various genres with a combined length of nearly 20 h.read more
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Proceedings ArticleDOI
vitrivr: A Flexible Retrieval Stack Supporting Multiple Query Modes for Searching in Multimedia Collections
TL;DR: The vitrivr architecture is self-contained and addresses all aspects of multimedia search, from offline feature extraction, database management to frontend user interaction, and thus offers a large variety of different query modes which can be seamlessly combined.
Book ChapterDOI
Combining Boolean and Multimedia Retrieval in vitrivr for Large-Scale Video Search
TL;DR: This paper presents the most recent additions to the vitrivr multimedia retrieval stack made in preparation for the participation to the 9\(^{th}\) Video Browser Showdown (VBS) in 2020.
Book ChapterDOI
Towards Explainable Interactive Multi-modal Video Retrieval with Vitrivr.
Silvan Heller,Ralph Gasser,Cristina Illi,Maurizio Pasquinelli,Loris Sauter,Florian Spiess,Heiko Schuldt +6 more
TL;DR: In this article, the most recent iteration of the vitrivr multimedia retrieval system for its participation in the Video Browser Showdown (VBS) 2021 was presented, which overhaul query formulation and results presentation for queries which specify temporal context, extend our database with index structures for similarity search and present experimental functionality aimed at improving the explainability of results with the objective of better supporting users in the selection of results and the provision of relevance feedback.
Proceedings ArticleDOI
Interactive Lifelog Retrieval with vitrivr
TL;DR: This paper shows how the new dataset can be used for retrieval in different modalities without sacrificing efficiency, and describes two recent additions, temporal scoring and staged querying, and discusses the deep learning methods used to enrich the dataset.
Proceedings ArticleDOI
Exploring Intuitive Lifelog Retrieval and Interaction Modes in Virtual Reality with vitrivr-VR
Florian Spiess,Ralph Gasser,Silvan Heller,Luca Rossetto,Loris Sauter,Milan van Zanten,Heiko Schuldt +6 more
TL;DR: The vitrivr-VR system as mentioned in this paper leverages the 3D space in virtual reality (VR) to offer novel retrieval and user interaction models, which are described with a special focus on design decisions taken for the participation in the Lifelog Search Challenge.
References
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Proceedings ArticleDOI
Cineast: A Multi-feature Sketch-Based Video Retrieval Engine
TL;DR: Cineast is a multi-feature sketch-based video retrieval engine able to universally retrieve video (sequences) without the need for prior knowledge or semantic understanding and to support powerful search paradigms in large video collections on the basis of user-provided sketches as query input.
Proceedings ArticleDOI
ADAM - A Database and Information Retrieval System for Big Multimedia Collections
TL;DR: ADAM is introduced, a novel multimedia retrieval system that is tailored to large collections and that is able to support both Boolean retrieval for structured data and similarity-based retrieval for feature vectors extracted from the multimedia objects.