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) " More
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
Manuscript profile
