Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance

By Minh N. Do, Martin Vetterli. IEEE Transactions on Image Processing, 2002

1,320 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/147e93184cd4b13a1423dbe5bca0c706cbc5b978

How researchers see this paper

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In Computer Vision and Pattern Recognition

1. A. Bovik
2. Zhou Wang
3. Dacheng Tao
4. Luc Van Gool
5. Chen Change Loy
6. Ming-Hsuan Yang
7. Lei Zhang
8. Radu Timofte

Citations in the corpus

• Content-based image retrieval: approaches and trends of the new age (2005, neutral): “similarity measurement using the Kullback-Leibler distance for statistical model comparison has been proposed in [26].”
• Universal Estimation of Information Measures for Analog Sources (2009, neutral): “In the area of image processing, divergence estimates have been applied to texture classification [77, 163, 266], shape and radiance estimation [84], and face recognition [12, 215].”
• Detecting False Data Injection Attacks in AC State Estimation (2015, methodology): “Reference [21] uses the KLD to retrieve texture.”
• A performance evaluation of gradient field HOG descriptor for sketch based image retrieval (2013, methodology): “Robust color blob-based matching was later proposed using spectral descriptors, such as wavelets [22, 23] which have more recently proven useful for more general image retrieval using colour texture [24, 25].”
• Directional multiscale modeling of images using the contourlet transform (2006, neutral): “(11) Retrieval based on KLD can be justified as retrieval by the maximum likelihood (ML) selection rule [33].”
• Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval (2012, methodology): “The application of the DWT using generalized Gaussian density with Kullback–Leibler distance has shown to provide efficient results for texture image retrieval [9] and image segmentation [10].”
• Modern Image Quality Assessment (2006, methodology): “This relationship between the KLD and log-likelihood functions has previously been used to compare images, mostly for classification and retrieval purposes [105– 108].”
• Directional multiresolution image representations (2002, methodology): “For comparison we also show the fitted GGD using the ML estimator [60].”
• Comparison of approximation methods to Kullback–Leibler divergence between Gaussian mixture models for satellite image retrieval (2016, methodology): “A novel wavelet-based texture retrieval method was developed by Do and Vetterli (2002) by using a closed-form Kullback–Leibler distance between the generalized Gaussian density distributions.”
• Quality-aware images (2006, methodology): “In previous work, a number of authors have pointed out the relationship between KLD and log-likelihood function and used KLD to compare images, mainly for classification and retrieval purposes [29]–[32].”
• Image retrieval: Ideas, influences, and trends of the new age (2008, neutral): “Examples of texture features using the wavelet transform and the discrete cosine transform can be found in [Do and Vetterli 2002; Li et al. 2000].”