School of Electrical, Computer and Energy Engineering
PhD Final Oral Defense
Texture Structure Analysis
by
Srenivas Varadarajan
Defense Date: 04/14/2014
Defense Time: 12.15 PM to 2.15 PM
Room: GWC 487
Committee:
Dr. Lina J. Karam
Dr. Chaitali Chakrabarti
Dr. Cihan Tepedenglue
Dr. Baoxin Li
Abstract
Texture analysis in general, plays an important role in applications like automated pattern
inspection, image and video compression, content based image retrieval, remote-sensing,
medical imaging and document processing. Texture Structure Analysis is the process of
studying the structure present in the textures and is the main focus of this work. This
structure can be expressed in terms of perceived regularity. In this work, the problem of
quantifying the degree of perceived regularity when looking at an arbitrary texture is
addressed. A no-reference perceptual texture regularity metric based on visual saliency is
proposed.
In the first part of the presentation, a brief overview on textures is given. The motivation
for understanding the texture regularity and its potential use in some image processing
and computer vision applications is envisaged. A brief introduction to visual saliency and
its potential impact in predicting the perceived regularity of textures is also enclosed.
In the second part of the presentation, the popular approaches for texture representation
and analysis are first reviewed. These include the statistical approaches, model-based
approaches, geometry-based approaches and structural approaches. Some of these
representations are used for texture regularity analysis which is a subfield of texture
analysis. A survey of some of the most popular works on texture regularity analysis is
made. Some of the drawbacks of these approaches are also presented. The importance of
a perceptually motivated approach for computing texture regularity is illustrated through
examples.
In the third part of the presentation, the performance of the most popular visual attention
models to predict the true visual saliency on textures is evaluated. The process of
building an eye-tracking database for establishing the ground-truth saliency in textures is
also described. Using the saliency map generated by the best visual attention model, the
proposed texture regularity metric is computed.
The proposed no-reference perceptual texture regularity metric is described in the fourth
part of this presentation. The proposed metric is expressed as a product of two textural
regularity scores. A textural similarity score quantifies the extent of similarity between
the primitives. A spatial distribution score quantifies the regularity in spatial placement
and the spatial spread of the primitive in a texture. It is shown through subjective testing
that the proposed metric has a strong correlation with the Mean Opinion Score for the
perceived regularity of textures. The method is also shown to outperform some of the
popular texture regularity metrics in predicting the perceived regularity.
Some of the extensions of the proposed metric to applications are enclosed in Chapters 5,
6 and 7. The ability of the proposed texture regularity metric to adaptively choose the
correct method for texture synthesis is demonstrated in Chapter 5. The chapter presents
the influence of texture regularity on the perceptual quality of textures synthesized
through parametric and non-parametric approaches. It is shown through subjective testing
that, textures with different degrees of perceived regularity exhibit different degrees of
vulnerability to synthesis artifacts. The work also proposes an algorithm for adaptively
selecting the appropriate texture synthesis algorithm.
A reduced reference texture quality metric for texture synthesis is proposed in Chapter 6.
The metric is based on the change in perceived regularity and change in perceived
granularity between the original and the synthesized textures. The perceived regularity is
quantified through the proposed texture regularity metric while the perceived granularity
is quantified through a new granularity metric is proposed in this work. It is shown
through subjective testing that the proposed metric has a strong correlation with the Mean
Opinion Score for the fidelity of synthesized textures and outperforms the state-of-the-art
full-reference quality metrics on 3 different texture databases.
The capability of the proposed regularity metric in predicting the perceptual degradation
of textures due to compression and blurriness artifacts is enclosed in Chapter 7. A
conclusive section, summarizing the salient contributions of this work and suggesting
some directions for future research is presented in the end.