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This paper introduces a system that automatically classifies image pairs based on the type of registration required to align them. The system uses support vector machines to classify between panoramas, high-dynamic-range images, focal stacks, super-resolution, and unrelated image pairs. A feature vector was developed to describe the images, and 1100 pairs were used to train and test the system with...
This paper presents a new method for satellite image classification. Specifically, we make two main contributions: (1) we introduce the sparse coding method for high-resolution satellite image classification; (2) we effectively combine a set of diverse and complementary features-SIFT, Color Histogram and Gabor to further improve the performance. A two-stage linear SVM classifier is designed for this...
A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is...
In this paper, we consider the problem of LSB matching steganalysis in grayscale images and provide a new detection method. We model the matching embedding as a kind of image degradation with certain additive pulse noise proportional to the embedding rate. We get the restored image by wavelet denoising as the cover imagepsilas estimation. Then we extract the features of 1D statistical grayscale histogram...
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