Journal Article10.1109/iscv.1995.476982
Efficient content-based image retrieval using automatic feature selection
D. Swets,Juyang Weng +1 more
- 21 Nov 1995
pp 85-90
56
TL;DR: Efficient content-based image retrieval using automatic feature selection for large image databases. The system utilizes optimal feature selection and a hierarchical image database for rapid retrieval.
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Abstract: We describe a self-organizing framework for content-based retrieval of images from large image databases at the object recognition level. The system uses the theories of optimal projection for optimal feature selection and a hierarchical image database for rapid retrieval rates. We demonstrate the query technique on a large database of widely varying real-world objects in natural settings, and show the applicability of the approach even for large variability within a particular object class.
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Citations
Toward integrating feature selection algorithms for classification and clustering
TL;DR: With the categorizing framework, the efforts toward-building an integrated system for intelligent feature selection are continued, and an illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms.
Image Retrieval
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Efficient Feature Selection via Analysis of Relevance and Redundancy
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A review of unsupervised feature selection methods
TL;DR: A comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature is provided and a taxonomy of these methods is presented.
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References
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Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
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Application of the Karhunen-Loeve procedure for the characterization of human faces
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TL;DR: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.
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•Proceedings Article
Viewbased and modular eigenspaces for face recognition
A. Pentland
- 01 Jan 1994
TL;DR: A modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer, which yields higher recognition rates as well as a more robust framework for face recognition.
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