A graph theoretic approach to scene matching

The ability to match two scenes is a fundamental requirement in a variety of computer vision tasks. A graph theoretic approach to inexact scene matching is presented which is useful in dealing with problems due to imperfect image segmentation. A scene is described by a set of graphs, with nodes representing objects and arcs representing relationships between objects. Each node has a set of values representing the relations between pairs of objects, such as angle, adjacency, or distance. With this method of scene representation, the task in scene matching is to match two sets of graphs. Because of segmentation errors, variations in camera angle, illumination, and other conditions, an exact match between the sets of observed and stored graphs is usually not possible. In the developed approach, the problem is represented as an association graph, in which each node represents a possible mapping of an observed region to a stored object, and each arc represents the compatibility of two mappings. Nodes and arcs have weights indicating the merit or a region-object mapping and the degree of compatibility between two mappings. A match between the two graphs corresponds to a clique, or fully connected subgraph, in the association graph. The task is to find the clique that represents the best match. Fuzzy relaxation is used to update the node weights using the contextual information contained in the arcs and neighboring nodes. This simplifies the evaluation of cliques. A method of handling oversegmentation and undersegmentation problems is also presented. The approach is tested with a set of realistic images which exhibit many types of sementation errors

Texture feature extraction and classification : a comparative study between traditional methods and deep learning : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Science in Computer Sciences at Massey University, Auckland, New Zealand

Figure 3.1 (=Kaehler & Bradski, 2017 Fig 1-4, p. 9) was removed for copyright reasons.Image classification has always been a core problem of computer vision. With the development of deep learning, it also provides a good solution for us to solve the problem of image feature extraction in image classification. In this thesis we used machine learning and convolutional neural network to study texture feature extraction and classification problems. We implemented a pipeline within the sklearn framework that utilized Local Binary Pattern (LBP) and Haralick as our feature descriptor and various classifiers (namely KNearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, Multilayer Perceptron, Gaussian Naive Bayes, Random Forest, AdaBoost, Logistic Regression and Decision Tree) to evaluate the performance on some popular texture datasets (Brodatz dataset, four extended Outex datasets and VisTex dataset). We also employed Linear Discriminant Analysis as our dimension reduction schema to observe the changes in classification accuracy. We also took advantage of Keras with TensorFlow backend framework and built a pipeline that uses ImageNet-trained convolutional neural network models to train and analyze classifier, extract image feature information and make predictions on test dataset samples. This allowed us to compare the results between traditional methods and CNN based methods. It was found that the classification accuracy has been greatly improved with the CNN based method