  In this paper, we introduce an adaptive unsupervised learning framework,
which utilizes natural images to train filter sets. The applicability of these
filter sets is demonstrated by evaluating their performance in two contrasting
applications - image quality assessment and texture retrieval. While assessing
image quality, the filters need to capture perceptual differences based on
dissimilarities between a reference image and its distorted version. In texture
retrieval, the filters need to assess similarity between texture images to
retrieve closest matching textures. Based on experiments, we show that the
filter responses span a set in which a monotonicity-based metric can measure
both the perceptual dissimilarity of natural images and the similarity of
texture images. In addition, we corrupt the images in the test set and
demonstrate that the proposed method leads to robust and reliable retrieval
performance compared to existing methods.
