Unsupervised Detection of Emergent Patterns in Large Image Collections

With the advent of modern image acquisition and sharing technologies, billions of images are added to the Internet every day. This huge repository contains useful information, but it is very hard to analyze. If labeled information is available for this data, then supervised learning techniques can be used to extract useful information. Visual pattern mining approaches provide a way to discover visual structures and patterns in an image collection without the need of any supervision. The Internet contains images of various objects, scenes, patterns, and shapes. The majority of approaches for visual pattern discovery, on the other hand, find patterns that are related to object or scene categories.Emergent pattern mining techniques provide a way to extract generic, complex and hidden structures in images. This thesis describes research, experiments, and analysis conducted to explore various approaches to mine emergent patterns from image collections in an unsupervised way. These approaches are based on itemset mining and graph theoretic strategies. The itemset mining strategy uses frequent itemset mining and rare itemset mining techniques to discover patterns.The mining is performed on a transactional dataset which is obtained from the BoW representation of images. The graph-based approach represents visual word co-occurrences obtained from images in a co-occurrence graph.Emergent patterns form dense clusters in this graph that are extracted using normalized cuts. The patterns that are discovered using itemset mining approaches are:stripes and parallel lines;dots and checks;bright dots;single lines;intersections; and frames. The graph based approach revealed various interesting patterns, including some patterns that are related to object categories

Unsupervised Detection of Emergent Patterns in Large Image Collections

With the advent of modern image acquisition and sharing technologies, billions of images are added to the Internet every day. This huge repository contains useful information, but it is very hard to analyze. If labeled information is available for this data, then supervised learning techniques can be used to extract useful information. Visual pattern mining approaches provide a way to discover visual structures and patterns in an image collection without the need of any supervision. The Internet contains images of various objects, scenes, patterns, and shapes. The majority of approaches for visual pattern discovery, on the other hand, find patterns that are related to object or scene categories.Emergent pattern mining techniques provide a way to extract generic, complex and hidden structures in images. This thesis describes research, experiments, and analysis conducted to explore various approaches to mine emergent patterns from image collections in an unsupervised way. These approaches are based on itemset mining and graph theoretic strategies. The itemset mining strategy uses frequent itemset mining and rare itemset mining techniques to discover patterns.The mining is performed on a transactional dataset which is obtained from the BoW representation of images. The graph-based approach represents visual word co-occurrences obtained from images in a co-occurrence graph.Emergent patterns form dense clusters in this graph that are extracted using normalized cuts. The patterns that are discovered using itemset mining approaches are:stripes and parallel lines;dots and checks;bright dots;single lines;intersections; and frames. The graph based approach revealed various interesting patterns, including some patterns that are related to object categories

A Feature Compression Scheme for Large Scale Image Retrieval Systems

Many image retrieval and object recognition systems rely on high-dimensional feature representation schemes such as SIFT. Because of this high dimensionality these features suffer from the curse of dimensionality and high memory needs. In this paper we evaluate an approach that reduces the size of a SIFT descriptor from 128 bytes to 128 bits. We test its performance in an image retrieval application and its robustness in the presence of various image transformations. We also introduce and evaluate a simpler approach that requires no training but requires 512 bits per descriptor

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

Abstract — 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. I