Emergent Semantic Patterns in Large Scale Image Dataset: A Datamining Approach

In this paper we investigate an unsupervised learning method applied to low level image features extracted from a large collection of images using data mining strategies. The mining process resulted in several interesting emergent semantic patterns. Initially, local image features are extracted using image processing techniques which are then clustered to generate a bag of words (BoW) for each image. These bags of words are then used for mining co-occurring patterns. The generated patterns were either global in nature i.e. showed a behavior spread across many images or a local and more rare behavior found across few images. These patterns are assigned semantic names to build a semantic relationship among images containing them.