Feature Extraction for Image Selection Using Machine Learning

Sammanfattning: During flights with manned or unmanned aircraft, continuous recording can result in avery high number of images to analyze and evaluate. To simplify image analysis and tominimize data link usage, appropriate images should be suggested for transfer and furtheranalysis. This thesis investigates features used for selection of images worthy of furtheranalysis using machine learning. The selection is done based on the criteria of havinggood quality, salient content and being unique compared to the other selected images.The investigation is approached by implementing two binary classifications, one regardingcontent and one regarding quality. The classifications are made using support vectormachines. For each of the classifications three feature extraction methods are performedand the results are compared against each other. The feature extraction methods used arehistograms of oriented gradients, features from the discrete cosine transform domain andfeatures extracted from a pre-trained convolutional neural network. The images classifiedas both good and salient are then clustered based on similarity measures retrieved usingcolor coherence vectors. One image from each cluster is retrieved and those are the resultingimages from the image selection. The performance of the selection is evaluated usingthe measures precision, recall and accuracy. The investigation showed that using featuresextracted from the discrete cosine transform provided the best results for the quality classification.For the content classification, features extracted from a convolutional neuralnetwork provided the best results. The similarity retrieval showed to be the weakest partand the entire system together provides an average accuracy of 83.99%.