Theory of keyblock-based image retrieval
ACM Transactions on Information Systems (TOIS)
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Region proximity in metric spaces and its use for approximate similarity search
ACM Transactions on Information Systems (TOIS)
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Mental image search by boolean composition of region categories
Multimedia Tools and Applications
MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
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Image similarity is typically evaluated by using low level features such as color histograms, textures, and shapes. Image similarity search algorithms require computing similarity between low level features of the query image and those of the images in the database. Even if state of the art access methods for similarity search reduce the set of features to be accessed and compared to the query, similarity search has still an high cost. In this paper we present a novel approach which processes image similarity search queries by using a technique that takes inspiration from text retrieval. We propose an approach that automatically indexes images by using visual terms chosen from a visual lexicon. Each visual term represents a typology of visual regions, according to various criteria. The visual lexicon is obtained by analyzing a training set of images, to infer which are the relevant typology of visual regions.We have defined a weighting and matching schema that are able respectively to associate visual terms with images and to compare images by means of the associated terms. We show that the proposed approach do not lose performance, in terms of effectiveness, with respect to other methods existing in literature, and at the same time offers higher performance, in terms of efficiency, given the possibility of using inverted files to support similarity searching.