Publications

@article{fernandez-robles_evaluation_2015,

title = {Evaluation of clustering configurations for object retrieval using sift features},

author = {Laura Fernández-Robles and Manuel Castejón-Limas and Javier Alfonso-Cendón and Enrique Alegre},

url = {https://link.springer.com/chapter/10.1007/978-3-319-12754-5_21},

year  = {2015},

date = {2015-01-01},

journal = {Project Management and Engineering: Selected Papers from the 17th International AEIPRO Congress held in Logroño, Spain, in 2013},

pages = {279–291},

abstract = {SIFT (Scale-Invariant Feature Transform) is a widely used keypoint descriptor for robust image matching and object recognition. It identifies distinctive features and matches them using a nearest-neighbor algorithm, followed by clustering with a Hough transform and pose verification via least-squares. However, the clustering choice lacks theoretical justification. This study explores and evaluates alternative clustering configurations based on keypoint pose parameters (x, y location, scale, and orientation).},

note = {Publisher: Springer International Publishing},

keywords = {ASASEC, Clustering, object recognition, SIFT},

pubstate = {published},

tppubtype = {article}

}