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Automatic Photography Categorization
Abstract
This thesis deals with content based automatic photo categorization. The aim of the work is to create an application, which is would be able to achieve sufficient precision and computation speed of categorization. Basic solution involves detection of interesting points, extraction of feature vectors, creation of visual codebook by clustering, using k-means algorithm and representing visual codebook by k-dimensional tree. Photography is represented by bag of words - histogram of presence of visual words in a particular photo. Support vector machines (SVM) was used in role of classifier. Afterwards the basic solution is enhanced by dividing picture into cells, which are processed separately, computing color correlograms for advanced image description, extraction of feature vectors in opponent color space and soft assignment of visual words to extracted feature vectors. The end of this thesis concerns to experiments of of above mentioned techniques and evaluation of the results of image categorization on their usage- info:eu-repo/semantics/bachelorThesis
- SURF; k-dimensional tree; color correlograms; knihovna OpenCV; tagování; experiments; experimenty; clustering; dividing of image; visual codebook; interesting points; klasifikátor; soft assignment; vizuální slovník; měkké přirazení; visual word; OpenCV library; lokální příznaky; bag of words; význačné body; photo categorization; tagging; barevné příznaky; color features; local features; shlukování; kategorizace fotografii; k-means; barevné korelogramy; classifier; k-dimenzionální strom; dělení obrazu; opponent color space; support vector machines; vizuální slovy