Efficiency Comparison of Supervised and Unsupervised Classifier on Content Based Classification using Shape, Color, Texture

Raikar, Poornima; Joshi, Sandeep

doi:10.1109/incet49848.2020.9154016

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2024

Publication Types

Select...

Article2

Other2

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Global and local features are implemented in [11]. BoVW was built using LBP and ORB with global features to form a single vector applied on the Flowers dataset with different classifiers.…”

Section: Related Workmentioning

confidence: 99%

Modified Bag of Visual Words Model for Image Classification

Sultani

Dhannoon

2021

ANJS

View full text Add to dashboard Cite

Image classification is acknowledged as one of the most critical and challenging tasks in computer vision. The bag of visual words (BoVW) model has proven to be very efficient for image classification tasks since it can effectively represent distinctive image features in vector space. In this paper, BoVW using Scale-Invariant Feature Transform (SIFT) and Oriented Fast and Rotated BRIEF(ORB) descriptors are adapted for image classification. We propose a novel image classification system using image local feature information obtained from both SIFT and ORB local feature descriptors. As a result, the constructed SO-BoVW model presents highly discriminative features, enhancing the classification performance. Experiments on Caltech-101 and flowers dataset prove the effectiveness of the proposed method.

show abstract

“…Global and local features are implemented in [11]. BoVW was built using LBP and ORB with global features to form a single vector applied on the Flowers dataset with different classifiers.…”

Section: Related Workmentioning

confidence: 99%