A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Dimensionality reduction for similarity searching in dynamic databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
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
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A content based image retrieval system based on the fuzzy ARTMAP architecture
Proceedings of the 12th annual ACM international conference on Multimedia
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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This study proposes an object-based image retrieval framework, called, ORF-NT, which trains a discriminative feature set for each object class and introduces a neighborhood tree for object labelling. For this purpose, initially, a large variety of features are extracted from the regions of the pre-segmented images. These features are, then, fed to a training module to select the ‘important‘ features, suppressing relatively less important ones for each class. ORF-NT (Object-based Image Retrieval Framework using Neighborhood Trees) defines a neighborhood tree for identifying the whole object from over-segmented regions. The neighborhood tree consists of the nodes corresponding to the neighboring regions as its children and merges the regions through a search algorithm. Experiments are performed on Corel database using MPEG-7 features in order to observe the power and the weakness of ORF-NT. The training phase, is tested by using Fuzzy ARTMAP [1], Euclidean distance and Adaboost algorithms [2]. It is observed that Fuzzy ARTMAP yields better retrieval rates than Euclidean distance and Adaboost algorithms.