Article Code : 13950803192273717(DOI : 10.7508/jist.2017.19.004)

Article Title : Concept Detection in Images Using SVD Features and Multi-Granularity Partitioning and Classification

Journal Number : 19 Summer 2017

Visited : 425

Files : 946 KB

List of Authors

  Full Name Email Grade Degree Corresponding Author
1 Kamran Farajzadeh Post Graduate Student Graduate Student
2 Esmail Zarezadeh Post Graduate Student Graduate Student
3 Jafar Mansouri Graduate PhD


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