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.
|Title of host publication||Image Analysis : 17th Scandinavian Conference, SCIA 2011 - Ystad, Sweden, May 2011 - Proceedings|
|Publication status||Published - 2011|
|Event||17th Scandinavian Conference on Image Analysis (SCIA) - Ystad Saltsjöbad, Ystad, Sweden|
Duration: 23 May 2011 → 27 May 2011
|Conference||17th Scandinavian Conference on Image Analysis (SCIA)|
|Period||23/05/2011 → 27/05/2011|
|Series||Lecture Notes in Computer Science|