An Efficient BoF Representation for Object Classification
Vinoharan, Veerapathirapillai (University of Jaffna)
Ramanan, Amirthalingam (University of Jaffna, Department of Computer Science)

Fecha:	2021
Resumen:	The Bag-of-features (BoF) approach has proved to yield better performance in a patch-based object classification system owing to its simplicity. However, often the very large number of patch-based descriptors (such as scale-invariant feature transform and speeded up robust features, extracted from images to create a BoF vector) leads to huge computational cost and an increased storage requirement. This paper demonstrates a two-staged approach to creating a discriminative and compact BoF representation for object classification. As a preprocessing stage to the codebook construction, ambiguous patch-based descriptors are eliminated using an entropy-based and one-pass feature selection approach, to retain high-quality descriptors. As a post-processing stage to the codebook construction, a subset of codewords which is not activated enough in images are eliminated from the initially constructed codebook based on statistical measures. Finally, each patch-based descriptor of an image is assigned to the closest codeword to create a histogram representation. One-versus-all support vector machine is applied to classify the histogram representation. The proposed methods are evaluated on benchmark image datasets. Testing results show that the proposed methods enables the codebook to be more discriminative and compact in moderate sized visual object classification tasks.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Bag-of-features ; Compact codebook ; Codeword selection ; Feature selection ; Computer vision ; Image processing ; Machine learning ; Pattern recognition
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 20 Núm. 2 (2021) , p. 51-68 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1403
DOI: 10.5565/rev/elcvia.1403

18 p, 498.2 KB

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