On Inferring Image Label Information Using Rank Minimization for Supervised Concept Embedding

Dmitriy Bespalov, Anders Lindbjerg Dahl, Bing Bai, Ali Shokoufandeh

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    Concept-based representation —combined with some classifier (e.g., support vector machine) or regression analysis (e.g., linear regression)—induces a popular approach among image processing community, used to infer image labels. We propose a supervised learning procedure to obtain an embedding to a latent concept space with the pre-defined inner product. This learning procedure uses rank minimization of the sought inner product matrix, defined in the original concept space, to find an embedding to a new low dimensional space. The empirical evidence show that the proposed supervised learning method can be used in combination with another computational image embedding procedure, such as bag-of-features method, to significantly improve accuracy of label inference, while producing embedding of low complexity.
    Original languageEnglish
    Title of host publicationImage Analysis : 17th Scandinavian Conference, SCIA 2011 - Ystad, Sweden, May 2011 - Proceedings
    Publication date2011
    ISBN (Print)978-3-642-21226-0
    Publication statusPublished - 2011
    Event17th Scandinavian Conference on Image Analysis (SCIA) - Ystad Saltsjöbad, Ystad, Sweden
    Duration: 23 May 201127 May 2011


    Conference17th Scandinavian Conference on Image Analysis (SCIA)
    LocationYstad Saltsjöbad
    Internet address
    SeriesLecture Notes in Computer Science

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