Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
A Perspective View on Visual Information Retrieval Systems
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Categorization of natural scenes: local vs. global information
APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Wavelet transforms and neural networks applied to image retrieval
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
Performance evaluation and optimization for content-based image retrieval
Pattern Recognition
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
Content Based Image Retrieval Using Color, Texture and Shape Features
ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Finding Images with Similar Lighting Conditions in Large Photo Collections
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Scene Retrieval of Natural Images
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Region-based image retrieval system with heuristic pre-clustering relevance feedback
Expert Systems with Applications: An International Journal
Unsupervised Image Retrieval with Similar Lighting Conditions
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Unsupervised image retrieval framework based on rule base system
Expert Systems with Applications: An International Journal
Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble
Expert Systems with Applications: An International Journal
Expert system design using wavelet and color vocabulary trees for image retrieval
Expert Systems with Applications: An International Journal
Fast K-means algorithm based on a level histogram for image retrieval
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this work we describe a new statistically-based methodology to organize and retrieve images of natural scenes by combining feature extraction, automatic clustering, automatic indexing and classification techniques. Our proposal belongs to the content-based image retrieval (CBIR) category. Our goal is to retrieve images from an image database by their content. The methodology combines randomly extracted points for feature extraction. The describing features are the mean, the standard deviation and the homogeneity (from the co-occurrence matrix) of a sub-image extracted from the three color channels (HSI). A K-means algorithm and a 1-NN classifier are used to build an indexed database. Three databases of images of natural scenes are used during the training and testing processes. One of the advantages of our proposal is that the images are not labeled manually for their retrieval. The performance of our framework is shown through several experimental results, including a comparison with several classifiers and comparison with related works, achieving up to 100% good recognition. Additionally, our proposal includes scene retrieval.