A parallel cross-modal search engine over large-scale multimedia collections with interactive relevance feedback

Wyl, Marc von; Hammami, Mohamed; Bruno, Éric; Marchand-Maillet, Stéphane

doi:10.1145/1991996.1992069

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…With spoken text, videos also have a possibility to extract semantic information from the sound. The most common application for video retrieval is large-scale audiovisual collection management [92,135]. Evaluation of video retrieval is also very active and standardized, with important contributions from TRECVID, 5 videoCLEF [81,82], and MultimediaEval.…”

Section: Space and Time Volumetric Datamentioning

confidence: 99%

“…One of the most frequently found methods in supervised learning are Support Vector Machines (SVM) [21] that also lead to best results in many visual information retrieval benchmarks [93]. Another trend in supervised learning are relevance feedback methods, where the retrieval system evolves by using the manual feedback from the user [59,135].…”

Section: Slice or Framementioning

confidence: 99%

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Retrieval of high-dimensional visual data: current state, trends and challenges ahead

Foncubierta–Rodríguez

Müller

Depeursinge

2013

Multimed Tools Appl

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Information retrieval algorithms have changed the way we manage and use various data sources, such as images, music or multimedia collections. First, free text information of documents from varying sources became accessible in addition to structured data in databases, initially for exact search and then for more probabilistic models. Novel approaches enable content-based visual search of images using computerized image analysis making visual image content searchable without requiring high quality manual annotations. Other multimedia data followed such as video and music retrieval, sometimes based on techniques such as extracting objects and classifying genre. 3D (surface) objects and solid textures have also been produced in quickly increasing quantities, for example in medical tomographic imaging. For these two types of 3D information sources, systems have become available to characterize the objects or textures and search for similar visual content in large databases. With 3D moving sequences (i.e., 4D), in particular medical imaging, even higherdimensional data have become available for analysis and retrieval and currently present many multimedia retrieval challenges. This article systematically reviews current techniques in various fields of 3D and 4D visual information retrieval and analyses the currently dominating application areas. The employed techniques are analysed and regrouped to highlight similarities and complementarities among them in order to guide the choice of optimal approaches for new 3D and 4D retrieval problems. Opportunities for future applications conclude the article. 3D or higher-dimensional visual information retrieval is expected to grow quickly in the coming years and in this respect this article can serve as a basis for designing new applications.

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