Tag suggestion and localization for web videos by bipartite graph matching

Chu, Wei-Ta; Li, Cheng-Jung; Chou, Yu-Chen

doi:10.1145/2072609.2072621

Cited by 13 publications

(6 citation statements)

References 20 publications

(28 reference statements)

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“…Some researchers have proposed automatic, or semi-automatic processes, that based on relevant frames [27][28][29] and similarity between frames, as well as relationships between them [30,31], can suggest and add new tags to be associated to videos on a keyframe/scene level.…”

Section: Content Analysis Approachesmentioning

confidence: 99%

“…The developed algorithm suggests new tags that can be associated to video content, based on the visual similarity of frames extracted from social websites like YouTube and Flickr. Other systems as [31,33] use similar approaches based on social knowledge, and combine visual similarity, tag frequency and geo-localization to suggest or create new and relevant tags to enrich the annotation quality.…”

Section: Content Analysis Approachesmentioning

confidence: 99%

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A collaborative approach for semantic time-based video annotation using gamification

Viana

Pinto

2017

Hum. Cent. Comput. Inf. Sci.

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BackgroundTagging systems enable new modalities of social communication and opportunities for data mining [1]. By being engaged in the annotation process, humans contribute to index information, and it is likely that they are attracted to retrieve information as well. However, a great part of that user-generated annotations have no quality-control process that guarantees the effectiveness of the data collected. Tags created by single users are typically noisy and selfish, contain misspelled words, miss important keywords and are not linked to specific timecodes [1][2][3]. This limits the usefulness of the tags, especially for efficient access to large assets of video content, and, more concretely, it does not allow the accurate access to exact parts of a video.Techniques grounded on video processing are still mostly not feasible given the computer resources required and the difficulty to develop a reliable and universal system to any type of content. Nevertheless, random access to crucial points of videos is essential for retrieving the best content and data that fills our expectations.Many TV and radio broadcasters accumulate very large archives, with various degrees of granularity of content and available metadata. Some of them are very old, speechless Abstract Efficient access to large scale video assets, may it be our life memories in our hard drive or a broadcaster archive which the company is eager to sell, requires content to be conveniently annotated. Manually annotating video content is, however, an intellectually expensive and time-consuming process. In this paper we argue that crowdsourcing, an approach that relies on a remote task force to perform activities that are costly or time-consuming using traditional methods, is a suitable alternative and we describe a solution based on gamification mechanisms for collaboratively collecting timed metadata. Tags introduced by registered players are validated based on a collaborative scoring mechanism that excludes erratic annotations. Voting mechanisms, enabling users to approve or refuse existing tags, provide an extra guarantee on the quality of the annotations. The sense of community is also created as users may watch the crowd's favourite moments of the video provided by a summarization functionality. The system was tested with a pool of volunteers in order to evaluate the quality of the contributions. The results suggest that crowdsourced annotation can describe objects, persons, places, etc. correctly, as well as be very accurate in time. Viana and Pinto Hum. Cent. Comput. Inf. Sci. (2017) Cent. Comput. Inf. Sci. (2017) 7:13 and do not have metadata associated. Most of cataloguing efforts have been geared towards reuse, to enable programme makers to easily find snippets of content to include in their own programmes. However, creating this metadata is a timely and expensive process, still relying on experts that continuously verify and sort the contents to be published, providing structured information for describing the content as a whole, as wel...

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Section: Content Analysis Approachesmentioning

confidence: 99%

Section: Content Analysis Approachesmentioning

confidence: 99%

A collaborative approach for semantic time-based video annotation using gamification

Viana

Pinto

2017

Hum. Cent. Comput. Inf. Sci.

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“…A bipartite graph can be constructed based on sets of views, and the same idea is used to measure similarity between 3D models [19]. Chu et al [20] described video keyframes and text-based tags as bags of visual words, which were then treated as nodes to construct a bipartite graph linking two modalities. The maximum weight matching was then determined to facilitate tag suggestion and localization.…”

Section: Bipartite Graph Matching For MM Retrievalmentioning

confidence: 99%

“…We slightly modify the framework proposed in [20] to enable privacy-preserving video tag suggestion. This framework simultaneously accomplishes tag suggestion and location, as illustrated in Figure 5.…”

Section: Privacy-preserving Video Tag Suggestionmentioning

confidence: 99%

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A Privacy-Preserving Bipartite Graph Matching Framework for Multimedia Analysis and Retrieval

Chu

Chang

2015

Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

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The emergence of cloud computing provides an unlimited computation/storage for users, and yields new opportunities for multimedia analysis and retrieval research. However, privacy of users, e.g., search intention, may be leaked to the server and maliciously utilized by companies or individuals with animus. This paper presents a privacy-preserving multimedia analysis framework based on a widely-adopted structure, i.e., bipartite graph, so that multimedia analysis and retrieval in the encrypted domain is enabled. This work aims to keep the server unaware of what the user wants to retrieve, and at the same time take advantage of the server's computation power. Homomorphic encryption schemes and communication protocols in the encrypted domain are integrated to facilitate bipartite graph construction and implement the Hungarian algorithm to find the best matching. Two applications, video tag suggestion and video copy detection, are developed on top of the privacy-preserving framework, and the evaluation results demonstrate that performance obtained in the encrypted domain is comparable with that obtained in the plain text domain.

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DUT-WEBV: A Benchmark Dataset for Performance Evaluation of Tag Localization for Web Video

Lei

Guan

et al. 2013

Lecture Notes in Computer Science

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Tag suggestion and localization for web videos by bipartite graph matching

Cited by 13 publications

References 20 publications

A collaborative approach for semantic time-based video annotation using gamification

A collaborative approach for semantic time-based video annotation using gamification

A Privacy-Preserving Bipartite Graph Matching Framework for Multimedia Analysis and Retrieval

DUT-WEBV: A Benchmark Dataset for Performance Evaluation of Tag Localization for Web Video

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