Image processing and understanding based on graph similarity testing: algorithm design and software development

Image processing and understanding is a key task in the human visual system. Among all related topics, content based image retrieval and classification is the most typical and important problem. Successful image retrieval/classification models require an effective fundamental step of image representation and feature extraction. While traditional methods are not capable of capturing all structural information on the image, using graph to represent the image is not only biologically plausible but also has certain advantages. Graphs have been widely used in image related applications. Traditional graph-based image analysis models include pixel-based graph-cut techniques for image segmentation, low-level and high-level image feature extraction based on graph statistics and other related approaches which utilize the idea of graph similarity testing. To compare the images through their graph representations, a graph similarity testing algorithm is essential. Most of the existing graph similarity measurement tools are not designed for generic tasks such as image classification and retrieval, and some other models are either not scalable or not always effective. Graph spectral theory is a powerful analytical tool for capturing and representing structural information of the graph, but to use it on image understanding remains a challenge. In this dissertation, we focus on developing fast and effective image analysis models based on the spectral graph theory and other graph related mathematical tools. We first propose a fast graph similarity testing method based on the idea of the heat content and the mathematical theory of diffusion over manifolds. We then demonstrate the ability of our similarity testing model by comparing random graphs and power law graphs. Based on our graph analysis model, we develop a graph-based image representation and understanding framework. We propose the image heat content feature at first and then discuss several approaches to further improve the model. The first component in our improved framework is a novel graph generation model. The proposed model greatly reduces the size of the traditional pixel-based image graph representation and is shown to still be effective in representing an image. Meanwhile, we propose and discuss several low-level and high-level image features based on spectral graph information, including oscillatory image heat content, weighted eigenvalues and weighted heat content spectrum. Experiments show that the proposed models are invariant to non-structural changes on images and perform well in standard image classification benchmarks. Furthermore, our image features are robust to small distortions and changes of viewpoint. The model is also capable of capturing important image structural information on the image and performs well alone or in combination with other traditional techniques. We then introduce two real world software development projects using graph-based image processing techniques in this dissertation. Finally, we discuss the pros, cons and the intuition of our proposed model by demonstrating the properties of the proposed image feature and the correlation between different image features

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields

Fuzzy Type-2 Trapezoid Methods for Decision Making Salt Farmer Mapping

The need for domestic salt every year has increased, both for consumption and industrial salt. Some of the fisheries service programs include providing assistance to people's businesses, providing geomembrane, and online marketing training. A large number of salt farmers and official work programs have caused the implementation of the program to be less than optimal, resulting in low salt production. This study uses a type-2 fuzzy method by integrating two methods, namely type-2 Fuzzy Analytical Hierarchy Process AHP (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Fuzzy type-2 has higher accuracy than fuzzy type-1 and is more efficient and more flexible in determining the linguistic scale for criteria. The Fuzzy Analytical Hierarchy Process AHP (FAHP) interval is used to determine the weight of the salt farmer mapping criteria. Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), used to determine. The findings of this study are that the indicators that most influence the mapping of salt farmers are land area, marketing, and market. The results of the mapping of salt farmers are the classification of salt farmer class groups and recommendations for improvement for each salt farmer. Hybrid type-2 Fuzzy Analytical Hierarchy Process AHP (FAHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), can be used for mapping salt farmers based on the consistency ratio value below 10 percent, 37 percent enter high class, 28 percent enter the middle class and 35 percent enter low clas

Methodology and potential of image analysis and unconventional use of GIS tools in determining grain size distribution and fractal dimension : a case study of fault rocks in the Western Tatra Mts. (Western Carpathians, Poland)

A methodology of textural analyses based on image analysis is proposed and tested based on study of fault rock samples from the Tatra Mts., Poland. The procedure encompasses: (1) SEM-BSE imagery of thin sections; (2) image classification using the maximum likelihood method, performed with GIS software; (3) statistical analysis and fractal dimension (self-similarity) analysis. The results of this method are comparable to those obtained with methods involving specialized software. The proposed analytical procedure particularly improves qualitative observations with quantitative data on grain shape and size distribution. The potential of the method is shown, as an auxiliary tool in determining the nature of deformation processes: the role of high-temperature dynamic recrystallization processes is recorded using grain shape indicators, whilst the switch from ductile to brittle conditions is reflected by the grain size distribution pattern

A Grounded Exploration of Sales and Distribution Channel Structures in Thirteen Industries in India Leading to a Classification Scheme

Innovation is a fundamental virtue of marketing. In this paper, a case is made to promote the use of innovative and novel combinations of research methodologies to derive new insights of business phenomena. This study is an attempt to understand and explain the sales and distribution channel structures in thirteen different industries in India. The investigation adopted a mix of case research and grounded theoretic research methodologies in exploring the subject under scrutiny. The study offers a classification scheme for grouping marketing channels into homogenous clusters based on similarity/dissimilarity using multivariate multidimensional mapping techniques. This scheme offers to explain the variety found in structures and suggests alternative channel possibilities. Such a scheme can be used in formulating marketing strategies and in deciding upon operational issues as well. While the main setting of the reported findings is Indian, the findings may prove to be useful beyond the national setting. Usual disclaimers associated with qualitative research methodology (Gummesson 1988) apply in this case concerning the generalisability and validity of the findings. This paper’s contribution is not as much in offering a schema as it is in suggesting an analytical plan/process that helps in visualising structures and associated strategies de novo.

Regularising Non-linear Models Using Feature Side-information

Very often features come with their own vectorial descriptions which provide detailed information about their properties. We refer to these vectorial descriptions as feature side-information. In the standard learning scenario, input is represented as a vector of features and the feature side-information is most often ignored or used only for feature selection prior to model fitting. We believe that feature side-information which carries information about features intrinsic property will help improve model prediction if used in a proper way during learning process. In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance. We control the structures of the learned models so that they reflect features similarities as these are defined on the basis of the side-information. We perform experiments on a number of benchmark datasets which show significant predictive performance gains, over a number of baselines, as a result of the exploitation of the side-information.Comment: 11 page with appendi