A note on binary template matching
Pattern Recognition
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Color and spatial feature for content-based image retrieval
Pattern Recognition Letters
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
A scheme of colour image retrieval from databases
Pattern Recognition Letters
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Metrics for Image Database Navigation
Perceptual Metrics for Image Database Navigation
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: A System for Region-Based Image Indexing and Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Region-based image retrieval using integrated color, shape, and location index
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
IEEE Transactions on Image Processing
Matching and retrieval based on the vocabulary and grammar of color patterns
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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This paper proposes a content based image retrieval system that uses semantic labels for determining image similarity. Thus, it aims to bridge the semantic gap between human perception and low-level features. Our approach works in two stages. Image segments, obtained from a subset of images in the database by an adaptive k-means clustering algorithm, are labelled manually during the training stage. The training information is used to label all the images in the database during the second stage. When a query is given, it is also segmented and each segment is labelled using the information available from the training stage. Similarity score between the query and a database image is based on the labels associated with the two images. Our results on two test databases show that region labelling helps in increasing the retrieval precision when compared to feature-based matching.