title: Mining visual collocation patterns via self-supervised subspace learning.
abstract: Traditional text data mining techniques are not directly applicable to image data which contain spatial information and are characterized by high-dimensional visual features. It is not a trivial task to discover meaningful visual patterns from images because the content variations and spatial dependence in visual data greatly challenge most existing data mining methods. This paper presents a novel approach to coping with these difficulties for mining visual collocation patterns. Specifically, the novelty of this work lies in the following new contributions: 1) a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and 2) a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via subspace learning. The experimental results show that our method can discover semantically meaningful patterns efficiently and effectively.
authors.affiliation: School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798. jsyuan@ntu.edu.sg
authors.forename: Junsong
authors.initials: J
authors.lastname: Yuan
authors.forename: Ying
authors.initials: Y
authors.lastname: Wu
mesh_terms: D000401:Aircraft; D001336:Automobiles; D056667:Biometric Identification; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis