Image block classification and variable block size segmentation using a model-fitting criterion

Won, Chee Sun; Park, Dong Kwon

doi:10.1117/1.601441

Cited by 10 publications

(1 citation statement)

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“…Ravishankar Lohse et.al [4] proposed a texture naming system that classifies textures into meaningful, hierarchical structured categories. Won and Park [7] used simple block transform based coding schemes for detecting textures. The paper [19] makes use of Local Binary Pattern (LBP) which is a grayscale invariant texture primitive statistic.…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning for Texture Characterization

Goswami

Agarwal

Rao

2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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In this paper, we propose a novel method for image texture characterization. Characterization is governed by simple perceptual variations in relative orientations in terms of either no variations present or variations present as row specific, column specific or diagonal specific. This generalization is obtained by modeling the input as a whole or image blocks depending on the broader or narrow coverage respectively. Most of the texture characterization is done either keeping a specific domain (synthetic or natural images specific to a category) or is application specific (segmentation on specific benchmark dataset or image retrieval). Contrary to this, our method is not biased towards any domain or application; rather it acts as a pre-processing step for guiding towards locating both non-textural and orientation specific textural image blocks. The proposed method quantifies the texture-tonal characterization of an image or image blocks using statistical ANOVA grading system. Once the grading for abstraction is assigned for both image as a whole and also for image blocks, the decision as to which higher-level algorithms need to be implemented on which block will become easier. The proposed method can be considered to be a three stage process -progressive sampling, image partitioning in blocks, ANOVA analysis and grading.

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Section: Introductionmentioning

confidence: 99%