Video Summarization: Techniques and Classification

Ajmal, Muhammad; Ashraf, Muhammad Husnain; Shakir, Muhammad; Abbas, Yasir; Shah, Faheem

doi:10.1007/978-3-642-33564-8_1

Cited by 86 publications

(32 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to keyframe selection, video summarization aims to find the most meaningful/interesting video segments, which can help users to skim long video sequences. Ajmal et al [19] gives an anatomy of video summarization methods, we refer interested readers to [19] for more details.…”

Section: Video Summarizationmentioning

confidence: 99%

Efficient video collection association using geometry-aware Bag-of-Iconics representations

Wang

Dunn

Rodríguez

et al. 2017

IPSJ T Comput Vis Appl

View full text Add to dashboard Cite

Recent years have witnessed the dramatic evolution in visual data volume and processing capabilities. For example, technical advances have enabled 3D modeling from large-scale crowdsourced photo collections. Compared to static image datasets, exploration and exploitation of Internet video collections are still largely unsolved. To address this challenge, we first propose to represent video contents using a histogram representation of iconic imagery attained from relevant visual datasets. We then develop a data-driven framework for a fully unsupervised extraction of such representations. Our novel Bag-of-Iconics (BoI) representation efficiently analyzes individual videos within a large-scale video collection. We demonstrate our proposed BoI representation with two novel applications: (1) finding video sequences connecting adjacent landmarks and aligning reconstructed 3D models and (2) retrieving geometrically relevant clips from video collections. Results on crowdsourced datasets illustrate the efficiency and effectiveness of our proposed Bag-of-Iconics representation.

show abstract

Section: Video Summarizationmentioning

confidence: 99%

Efficient video collection association using geometry-aware Bag-of-Iconics representations

Wang

Dunn

Rodríguez

et al. 2017

IPSJ T Comput Vis Appl

View full text Add to dashboard Cite

show abstract

“…A lot of research has been done in the area of video summarization. Ajmal et al [2], for example, give an overview of the different techniques and classification methods that are commonly used in literature, e.g., feature classification [3], clustering [4], shot selection [5] and trajectory analysis [6]. The focus of this paper will be on shot selection and detection [7][8][9].…”

Section: Video Summarizationmentioning

confidence: 99%

Spatio-Temporal Wardrobe Generation of Actors’ Clothing in Video Content

Vandecasteele

Vervaeke

Vandersmissen

et al. 2016

Human-Computer Interaction. Novel User Experiences

View full text Add to dashboard Cite

“…The survey [11] gives a recent and detailed summary of video summarization techniques similar to the techniques described until here, as well as other summarization approaches that are not considered in this project. However, no other face detection based methods are mentioned.…”

Section: Key-frame Selection Based Indexingmentioning

confidence: 99%

Monitoring Interactions

Meißner¹,

Veltkamp²

2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

This work proposes a human interaction recognition based approach to video indexing that represents a video by showing when and with whom was interacted throughout the video. In order to visualize the length of an interaction, it is required to recognize individuals that have been detected in earlier parts of the video. To solve this problem, an approach to photo-clustering is extended to video material by tracking detected faces and using the information from tracking to improve the recognition of human beings. The results of the tracking based approach show a considerable decrease of false cluster assignments compared to the original method. Further, it is demonstrated that the proposed method is able to correctly recognize the appearance of ve out of the six individuals correctly.iii iv

show abstract

Video Summarization: Techniques and Classification

Cited by 86 publications

References 33 publications

Efficient video collection association using geometry-aware Bag-of-Iconics representations

Efficient video collection association using geometry-aware Bag-of-Iconics representations

Spatio-Temporal Wardrobe Generation of Actors’ Clothing in Video Content

Monitoring Interactions

Contact Info

Product

Resources

About