In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to build a computational model to automatically predict human perceived image quality without a reference image and without knowing the distortion present in the image. Previous approaches for this problem typically rely on hand-crafted features which are carefully designed based on prior knowledge.In contrast, we use raw-image-patches extracted from a set of unlabeled images to learn a dictionary in an unsupervised manner. We use soft-assignment coding with max pooling to obtain effective image representations for quality estimation. The proposed algorithm is very computationally appealing, using raw image patches as local descriptors and using soft-assignment for encoding. Furthermore, unlike previous methods, our unsupervised feature learning strategy enables our method to adapt to different domains. CORNIA (Codebook Representation for No-Reference Image Assessment) is tested on LIVE database and shown to perform statistically better than the full-reference quality measure, structural similarity index (SSIM) and is shown to be comparable to state-of-the-art general purpose NR-IQA algorithms.
In this paper, we present a learning based approach for computing structural similarities among document images for unsupervised exploration in large document collections. The approach is based on multiple levels of content and structure. At a local level, a bag-of-visual words based on SURF features provides an effective way of computing content similarity. The document is then recursively partitioned and a histogram of codewords is computed for each partition. Structural similarity is computed using a random forest classifier trained with these histogram features. We experiment with three diverse datasets of document images varying in size, degree of structural similarity, and types of document images. Our results demonstrate that the proposed approach provides an effective general framework for grouping structurally similar document images.
I. INTRODUCTIONAssociating images with similar structural characteristics is a first and important step in mining visual information from large collections of images. Having an effective image similarity measure makes searching and browsing large repositories of images easier as clustered images can be collectively tagged with attributes and indexed for search and retrieval tasks. Clustering search results for queries on document image collections, or performing near-duplicate detection are examples of applications where good similarity measures are needed.
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