Near Sets in Set Pattern Classification
Uchime, Chidoteremndu Chinonyelum
This research is focused on the extraction of visual set patterns in digital images, using relational properties like nearness and similarity measures, as well as descriptive properties such as texture, colour and image gradient directions. The problem considered in this thesis is application of topology in visual set pattern discovery, and consequently pattern generation. A visual set pattern is a collection of motif patterns generated from different unique points called seed motifs in the set. Each motif pattern is a descriptive neighbourhood of a seed motif. Such a neighbourhood is a set of points that are descriptively near a seed motif. A new similarity distance measure based on dot product between image feature vectors was introduced in this research, for image classification with the generated visual set patterns. An application of this approach to pattern generation can be useful in content based image retrieval and image classification.
pattern, pattern generation, pattern recognition, proximity space, visual set pattern, motif pattern, seed motif, description, perception, probe function, feature vector, binary classification, K-means, CBIR, salient, similarity measure, sensitivity, specificity