Content-Based Video Retrieval in Historical Collections of the German Broadcasting Archive

Mühling, Markus; Meister, Manja; Korfhage, Nikolaus; Wehling, Jörg; Hörth, Angelika; Ewerth, Ralph; Freisleben, Bernd

doi:10.1007/978-3-319-43997-6_6

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…Automatic labelling is a welldeveloped domain in visual analysis, with many tools like DVT capable of automatically producing metadata summarising the content (people/actors, dialogue, scenes, objects) and style (camera angle, lighting, framing, sound) of the images. For example, in a study carried out in large historical collections of the German Broadcasting Archive, an approach of content-based video retrieval has been adopted 24 . The aim is to automatically assign semantic tags to video shots.…”

Section: Crobora Metadata Of Video Sequencesmentioning

confidence: 99%

From Stock Shots to Ghost Data: Tracking Audiovisual Archives about the European Union

Shen,

Treleani,

Compagno

et al. 2023

VIEW Journal

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This paper deals with a major challenge linked to the collection of audiovisual documents within television and web archives. Looking for repeated sequences within a corpus of thousands of videos, we faced the fact that the footage we were looking for reveals itself to be reachable only as ghost data. In fact, any audiovisual sequence reused within different contexts exists conceptually as the repetition of one single visual unit, but from the point of view of the metadata tagging its occurrences, each item is a distinct document. Like a ghost, the shot is there, scattered among different places, but the metadata cannot point us to the visual form repeated, despite its evidence to the human viewer. When facing large amounts of data, to relate a visual unit to its occurrences, data analysis techniques are needed. We describe our procedures of collection and annotation, and the solutions combining qualitive work and a computer-aided approach to face this main challenge, within the research project Crossing Borders Archives (CROBORA).

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Section: Crobora Metadata Of Video Sequencesmentioning

confidence: 99%

From Stock Shots to Ghost Data: Tracking Audiovisual Archives about the European Union

Shen,

Treleani,

Compagno

et al. 2023

VIEW Journal

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“…ASM is an important information retrieval task, used to perform reverse video lookup for broadcasting, research, and copyright infringement monitoring [3,4].…”

Section: Automated Scene Matchingmentioning

confidence: 99%

Convolutional Hashing for Automated Scene Matching

Lončarić¹,

Liu²,

Weber³

2018

Preprint

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We present a powerful new loss function and training scheme for learning binary hash functions. In particular, we demonstrate our method by creating for the first time a neural network that outperforms state-of-the-art Haar wavelets and color layout descriptors at the task of automated scene matching. By accurately relating distance on the manifold of network outputs to distance in Hamming space, we achieve a 100-fold reduction in nontrivial false positive rate and significantly higher true positive rate. We expect our insights to provide large wins for hashing models applied to other information retrieval hashing tasks as well.

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“…Due to the enormous progress of multimedia and network technology, digital information has experienced rapid development in both quality and quantity. The need for an automatic video content analysis is urgently required in order to handle the huge amount of data for later applications such as annotation and video retrieval 1 . Video shot boundary detection (SBD) is the main process for any video processing application to break the video sequence into many useful shots.…”

Section: Introductionmentioning

confidence: 99%

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Mishra

2022

Concurrency and Computation

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Summary This paper presents a hybrid feature extraction and optimized deep learning model for the effective shot boundary detection (SBD). A cross‐guided bilateral filtering technique is used for pre‐processing. A hybrid fuzzy histogram with dual tree complex wavelet transform (FH‐DTCWT) is developed for the feature extraction to extract the visual features from the pre‐processed frames of the block. Candidate segment selection process is performed to identify the non‐boundary frames from the extracted features to improve the accuracy. Continuity matrix is created using the possible transition frames to verify that the frames are in sequential order without any gap. Classification of transition types are performed by the proposed optimized deep convolutional neural network. Optimized deep learning (DCNN‐RBESO) model is the combination of deep convolutional neural network (DCNN) and Rider bald eagle search optimization (RBESO) algorithm. To update the weight of DCNN model in learning, RBESO is utilized. MATLAB tool is used to develop the proposed SBD model. The experimental analysis is carried out for the proposed model using TREC Video Retrieval Evaluation (TRECVID) and VideoSeg datasets. The proposed model outperformed in the detection of shot boundaries in terms of precision, recall, and F1‐score.

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Content-Based Video Retrieval in Historical Collections of the German Broadcasting Archive

Cited by 9 publications

References 20 publications

From Stock Shots to Ghost Data: Tracking Audiovisual Archives about the European Union

From Stock Shots to Ghost Data: Tracking Audiovisual Archives about the European Union

Convolutional Hashing for Automated Scene Matching

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

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