Joint categorization of queries and clips for web-based video search

Zhang, Ruofei; Sarukkai, Ramesh R.; Chow, Jyh-Herng; Dai, Wei; Zhang, Zhongfei

doi:10.1145/1178677.1178705

Cited by 8 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang et al [20] classify videos with respect to five categories (movies, music, fun, finance and news) by using binary classifiers trained on two separated feature sets -meta-data (i.e., text) and content (i.e., visual). Their experiments demonstrated that category accuracy depends on the type of the classifier and therefore they take advantage of this prior knowledge by using a voting based category-dependent scheme.…”

Section: Video and Image Classificationmentioning

confidence: 99%

Improved video categorization from text metadata and user comments

Filippova

Hall

2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

We consider the task of assigning categories (e.g., howto/cooking, sports/basketball, pet/dogs) to YouTube 1 videos from video and text signals. We show that two complementary views on the data-from the video and text perspectives-complement each other and refine predictions. The contributions of the paper are threefold: (1) we show that a text-based classifier trained on imperfect predictions of the weakly supervised video content-based classifier is not redundant; (2) we demonstrate that a simple model which combines the predictions made by the two classifiers outperforms each of them taken independently; (3) we analyse such sources of text information as video title, description, user tags and viewers' comments and show that each of them provides valuable clues to the topic of the video.

show abstract

Section: Video and Image Classificationmentioning

confidence: 99%

Improved video categorization from text metadata and user comments

Filippova

Hall

2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

show abstract

“…The diverse and complex nature of Web videos enforced researchers to employ different combinations of modalities, such as audio, visual, and textual, for automatic Web video classification. Metadata and content‐based binary classifiers were designed to classify videos into five categories (music, movies, fun, news, and finance). Their experiments showed that the category accuracy relies on the type of the classifier, and hence they took advantage of this preceding knowledge by employing a voting‐based category‐dependent scheme.…”

Section: Related Workmentioning

confidence: 99%

Web video classification with visual and contextual semantics

Afzal

Shah

Tufail

2019

Int J Communication

View full text Add to dashboard Cite

Summary On the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.

show abstract

“…Query-Classes for Web Video Collections: Zhang et al [6] describe an application of query-class-dependency for video search in a domain entirely different from TRECVID: large-scale web-based video clip search. Web video clips are almost entirely different in content than the broadcast news clips in the TRECVID datasets, and the types of users (typical consumers for the web and news analysts or producers for TRECVID) are also quite different.…”

Section: B Multimodal Search Over Image and Video Collectionsmentioning

confidence: 99%

Query-Adaptive Fusion for Multimodal Search

2008

View full text Add to dashboard Cite

| We conduct a broad survey of query-adaptive search strategies in a variety of application domains, where the internal retrieval mechanisms used for search are adapted in response to the anticipated needs for each individual query experienced by the system. While these query-adaptive approaches can range from meta-search over text collections to multimodal search over video databases, we propose that all such systems can be framed and discussed in the context of a single, unified framework. In our paper, we keep an eye towards the domain of video search, where search cues are available from a rich set of modalities, including textual speech transcripts, low-level visual features, and high-level semantic concept detectors. The relative efficacy of each of the modalities is highly variant between many types of queries. We observe that the state of the art in query-adaptive retrieval frameworks for video collections is highly dependent upon the definition of classes of queries, which are groups of queries that share similar optimal search strategies, while many applications in text and web retrieval have included many advanced strategies, such as direct prediction of search method performance and inclusion of contextual cues from the searcher. We conclude that such advanced strategies previously developed for text retrieval have a broad range of possible applications in future research in multimodal video search.

show abstract

Joint categorization of queries and clips for web-based video search

Cited by 8 publications

References 35 publications

Improved video categorization from text metadata and user comments

Improved video categorization from text metadata and user comments

Web video classification with visual and contextual semantics

Query-Adaptive Fusion for Multimodal Search

Contact Info

Product

Resources

About