Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Structures and Algorithms
Data Structures and Algorithms
Integrated Browsing and Querying for Image Databases
IEEE MultiMedia
Emergent Semantics through Interaction in Image Databases
IEEE Transactions on Knowledge and Data Engineering
Finding Pictures of Objects in Large Collections of Images
ECCV '96 Proceedings of the International Workshop on Object Representation in Computer Vision II
Evaluation of key frame-based retrieval techniques for video
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Foundations and Trends in Information Retrieval
Organizing and browsing image search results based on conceptual and visual similarities
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Size matters! how thumbnail number, size, and motion influence mobile video retrieval
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Similarity-based image organization and browsing using multi-resolution self-organizing map
Image and Vision Computing
A large scale system for searching and browsing images from the world wide web
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Understanding Similarity Metrics in Neighbour-based Recommender Systems
Proceedings of the 2013 Conference on the Theory of Information Retrieval
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Given a collection of images and a set of image features, we can build what we have previously termed NNk networks by representing images as vertices of the network and by establishing arcs between any two images if and only if one is most similar to the other for some weighted combination of features. An earlier analysis of its structural properties revealed that the networks exhibit small-world properties, that is a small distance between any two vertices and a high degree of local structure. This paper extends our analysis. In order to provide a theoretical explanation of its remarkable properties, we investigate explicitly how images belonging to the same semantic class are distributed across the network. Images of the same class correspond to subgraphs of the network. We propose and motivate three topological properties which we expect these subgraphs to possess and which can be thought of as measures of their compactness. Measurements of these properties on two collections indicate that these subgraphs tend indeed to be highly compact.