In this work, 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.
- M.Prabhushankar, D.Temel, and G.AlRegib, “Generating adaptive and robust filter sets from an unsupervised learning framework,” submitted to The International Conference on Image Processing, Sept 2017. [PDF] [Poster/Lec] [Bib] [Code]
- D. Temel, M. Prabhushankar, and G. AlRegib, “UNIQUE: Unsupervised Image Quality Estimation,” in IEEE Signal Processing Letters , vol.23, no.10, pp.1414-1418, Oct. 2016. [PDF] [Poster/Lec] [Bib] [Code]
- M. Prabhushankar, D. Temel and G. AlRegib, “MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation,” to be presented at Image Quality and System Performance XIV, part of IS&T Electronic Imaging , San Francisco, CA, Jan. 29 – Feb. 2, 2017. [PDF] [Poster/Lec] [Bib] [Code]