Multidimensional access methods
ACM Computing Surveys (CSUR)
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Beta wavelets: synthesis and application to lossy image compression
Advances in Engineering Software - Advanced algorithms and architectures for signal processing
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Active semi-supervised fuzzy clustering for image database categorization
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multidimensional Binary Search Trees in Database Applications
IEEE Transactions on Software Engineering
Image coding using wavelet transform
IEEE Transactions on Image Processing
A novel image retrieval model based on the most relevant features
Knowledge-Based Systems
Classification improvement of local feature vectors over the KNN algorithm
Multimedia Tools and Applications
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One of the challenges in the development of a content-based multimedia indexing and retrieval application is to achieve an efficient indexing scheme. To retrieve a particular image from a large scale image database, users can be frustrated by the long query times. Conventional indexing structures cannot usually cope with the presence of a large amount of feature vectors in high-dimensional space. This paper addresses such problems and presents a novel indexing technique, the embedded lattices tree, which is designed to bring an effective solution especially for realizing the trade off between the retrieval speed up and precision. The embedded lattices tree is based on a lattice vector quantization algorithm that divides the feature vectors progressively into smaller partitions using a finer scaling factor. The efficiency of the similarity queries is significantly improved by using the hierarchy and the good algebraic and geometric properties of the lattice. Furthermore, the dimensionality reduction that we perform on the feature vectors, translating from an upper level to a lower one of the embedded tree, reduces the complexity of measuring similarity between feature vectors. In addition, it enhances the performance on nearest neighbor queries especially for high dimensions. Our experimental results show that the retrieval speed is significantly improved and the indexing structure shows no sign of degradations when the database size is increased.