Efficient image signatures and similarities using tensor products of local descriptors

In this paper, we introduce a novel image signature effective in both image retrieval and image classification. Our approach is based on the aggregation of tensor products of discriminant local features, named VLATs (vector of locally aggregated tensors). We also introduce techniques for the packing and the fast comparison of VLATs. We present connections between VLAT and methods like kernel on bags and Fisher vectors. Finally, we show the ability of our method to be effective for two different retrieval problems, thanks to experiments carried out on similarity search and classification datasets.     

Automated flexion crease identification using internal image seams

Palmar flexion crease recognition is a palmprint identification method for verifying biometric identity. This paper proposes a method of automated flexion crease recognition that can be used to identify palmar flexion creases in online palmprint images. A modified image seams algorithm is used to extract the flexion creases, and a matching algorithm, based on kd-tree nearest neighbour searching, is used to calculate the similarity between them. Experimental results show that in 1000 images from 100 palms, when compared to manually identified flexion creases, a genuine acceptance rate of 100% can be achieved, with a false acceptance rate of 0.0045%.