Fundamentals of digital image processing
Fundamentals of digital image processing
Statistical pattern recognition
Handbook of pattern recognition & computer vision
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Statistical structuring of pictorial databases for content-based image retrieval systems
Pattern Recognition Letters
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical color image region segmentation for content-based image retrieval system
IEEE Transactions on Image Processing
EdgeFlow: a technique for boundary detection and image segmentation
IEEE Transactions on Image Processing
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
Information Sciences: an International Journal
Self-eigenroughness selection for texture recognition using genetic algorithms
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Image annotation for adaptive enhancement of uncalibrated color images
VISUAL'05 Proceedings of the 8th international conference on Visual Information and Information Systems
Scene classification using a multi-resolution bag-of-features model
Pattern Recognition
Computational and space complexity analysis of SubXPCA
Pattern Recognition
Indoor scene recognition by a mobile robot through adaptive object detection
Robotics and Autonomous Systems
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For certain databases and classification tasks, analyzing images based on region features instead of image features results in more accurate classifications. We introduce eigenregions, which are geometrical features that encompass area, location, and shape properties of an image region, even if the region is spatially incoherent. Eigenregions are calculated using principal component analysis (PCA). On a database of 77,000 different regions obtained through the segmentation of 13,500 real-scene photographic images taken by nonprofessionals, eigenregions improved the detection of localized image classes by a noticeable amount. Additionally, eigenregions allow us to prove that the largest variance in natural image region geometry is due to its area and not to shape or position.