Hua, K. A., & Shaykhian, G. A., & Beil, R. J., & AKPINAR, K., & Martin, K. A. (2014, June), Saliency-Based CBIR System for Exploring Lunar Surface Imagery  Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--22998

\bibitem{asee_peer_22998}
  Hua, K. A., \& Shaykhian, G. A., \& Beil, R. J., \& AKPINAR, K., \& Martin, K. A. (2014, June), \emph{Saliency-Based CBIR System for Exploring Lunar Surface Imagery}
  Paper presented at 2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana.
  10.18260/1-2--22998

Kien A. Hua, Gholam Ali Shaykhian, Robert J Beil, KUTALMIS AKPINAR, and Kyle A. Martin.  "Saliency-Based CBIR System for Exploring Lunar Surface Imagery".  2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana, 2014, June.  ASEE Conferences, 2014.  https://peer.asee.org/22998 Internet.  14 Dec, 2024

\bibitem{asee_peer_22998}
  Kien A. Hua, Gholam Ali Shaykhian, Robert J Beil, KUTALMIS AKPINAR, and Kyle A. Martin.
  "Saliency-Based CBIR System for Exploring Lunar Surface Imagery".
  \emph{2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana, 2014, June}.
  ASEE Conferences, 2014.
  https://peer.asee.org/22998 Internet.  14 Dec, 2024

@INPROCEEDINGS{asee_peer_22998
author = "Kien A. Hua, Gholam Ali Shaykhian, Robert J Beil, KUTALMIS AKPINAR, and Kyle A. Martin"
title = "Saliency-Based CBIR System for Exploring Lunar Surface Imagery"
booktitle = "2014 ASEE Annual Conference \& Exposition"
year = "2014"
month = "June"
address = "Indianapolis, Indiana"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/22998}
number = {10.18260/1-2--22998}
}

TY  - CPAPER
AB  -       Saliency-Based CBIR System for Exploring Lunar Surface ImageryRecent NASA missions like the Lunar Reconnaissance Orbiter (LRO) have produced vastarchives of surface imagery which must be analyzed to locate landmarks with distinctive visualfeatures, like craters, which provide important information about geologic history and potentialmineral resources. Content Based Image Retrieval allows large archives of images to beefficiently queried based on visual content by indexing multidimensional feature vectorsextracted from the images. Unlike images of particular objects or scenes traditionally retrievedusing CBIR, surface images are not focused on any particular landmark so they must be pre-processed to identify Regions of Interest (ROI) to be indexed for retrieval. Previous workidentified ROIs using manual annotation and expensive detection algorithms for specific types oflandmarks, such as Crater Detection Algorithms (CDA). In contrast, this work utilizes a general-purpose saliency-based landmark detection algorithm for identifying ROIs which are thenindexed for retrieval using feature vectors extracted from the ROI images. We evaluate theretrieval performance of several feature vectors and assess the saliency-based landmark detectionperformance in comparison to a comprehensive crater database created using manual annotationand a CDA. Experimental results demonstrate the advantages of the general-purpose saliency-based CBIR system for exploring lunar surface imagery.
AU  - Kien A. Hua
AU  - Gholam Ali Shaykhian
AU  - Robert J Beil
AU  - KUTALMIS AKPINAR
AU  - Kyle A. Martin
CY  - Indianapolis, Indiana
DA  - 2014/06/15
PB  - ASEE Conferences
TI  - Saliency-Based CBIR System for Exploring Lunar Surface Imagery
UR  - https://peer.asee.org/22998
DO  - 10.18260/1-2--22998
ER  -