مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 5 19 2017 11 12 Concept Detection in Images Using SVD Features and Multi-Granularity Partitioning and Classification Concept Detection in Images Using SVD Features and Multi-Granularity Partitioning and Classification 172 182 10.7508/jist.2017.19.004 en Kamran Farajzadeh Islamic Azad University, North Tehran branch Esmail Zarezadeh Amir Kabir University Jafar Mansouri Ferdowsi university of Mashhad 2016 10 24 New visual and static features, namely, right singular feature vector, left singular feature vector and singular value feature vector are proposed for the semantic concept detection in images. These features are derived by applying singular value decomposition (SVD) "directly" to the "raw" images. In SVD features edge, color and texture information is integrated simultaneously and is sorted based on their importance for the concept detection. Feature extraction is performed in a multi-granularity partitioning manner. In contrast to the existing systems, classification is carried out for each grid partition of each granularity separately. This separates the effect of classifications on partitions with and without the target concept on each other. Since SVD features have high dimensionality, classification is carried out with K-nearest neighbor (K-NN) algorithm that utilizes a new and "stable" distance function, namely, multiplicative distance. Experimental results on PASCAL VOC and TRECVID datasets show the effectiveness of the proposed SVD features and multi-granularity partitioning and classification method http://jist.ir/fa/Article/Download/14972