Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework
This work addresses the need for versatile filter sets in computer vision applications like image quality assessment and texture retrieval, but it is incremental as it builds on existing unsupervised learning approaches.
The paper tackled the problem of generating adaptive filter sets for image quality assessment and texture retrieval using an unsupervised learning framework, achieving robust and reliable retrieval performance compared to existing methods, as demonstrated through experiments on corrupted test sets.
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.