Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Combining supervised learning with color correlograms for content-based image retrieval
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Colour and Texture Features for Content Based Image Retrieval
CGIV '06 Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation
Gabor Wavelet Correlogram Algorithm for Image Indexing and Retrieval
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Image retrieval system using R-tree self-organizing map
Data & Knowledge Engineering
Texture image retrieval using rotated wavelet filters
Pattern Recognition Letters
Automated binary texture feature sets for image retrieval
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Concept learning by fuzzy k-NN classification and relevance feedback for efficient image retrieval
Expert Systems with Applications: An International Journal
A smart content-based image retrieval system based on color and texture feature
Image and Vision Computing
Integrating spatial and color information in images using a statistical framework
Expert Systems with Applications: An International Journal
Expert system for color image retrieval
Expert Systems with Applications: An International Journal
Expert system based on artificial neural networks for content-based image retrieval
Expert Systems with Applications: An International Journal
Wavelet correlogram: A new approach for image indexing and retrieval
Pattern Recognition
Effective content-based video retrieval using pattern-indexing and matching techniques
Expert Systems with Applications: An International Journal
Region-based image retrieval system with heuristic pre-clustering relevance feedback
Expert Systems with Applications: An International Journal
Rotation-Invariant Texture Image Retrieval Using Rotated Complex Wavelet Filters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Novel Evolutionary Approach for Optimizing Content-Based Image Indexing Algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Self organizing natural scene image retrieval
Expert Systems with Applications: An International Journal
Image indexing using the color and bit pattern feature fusion
Journal of Visual Communication and Image Representation
| Hi-index | 12.06 |
A new image indexing and retrieval system for content based image retrieval (CBIR) is proposed in this paper. The characteristics (vector points) of image are computed using color (color histogram) and SOT (spatial orientation tree). The SOT defines the spatial parent-child relationship among wavelet coefficients in multi-resolution wavelet sub-bands. First the image is divided into sub-blocks and then constructed the SOT for each low pass wavelet coefficient is considered as a vector point of that particular image. Similarly the color histogram features are collected from the each sub-block. The vector points of each image are indexed using vocabulary tree. The retrieval results of the proposed method are tested on different image databases, i.e., natural image database consists of Corel 1000 (DB1), Brodatz texture image database (DB2) and MIT VisTex database (DB3). The results after being investigated show a significant improvement in terms of average precision, average recall and average retrieval rate on DB1 database and average retrieval rate on texture databases (DB2 and DB3) as compared with most of existing techniques on respective databases.