Investigating the relationship between the distribution of local semantic concepts and local keypoints for image annotation

Alqasrawi, Yousef; Neagu, Daniel

doi:10.1109/ukci.2014.6930165

Cited by 1 publication

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section reviews our hypothesis proposed in [22] which investigates the correlation between local semantic concepts and local keypoints, based on their distributions over all image regions. Local semantic concepts are labels assigned to image regions.…”

Section: Local Semantic Concepts Vs Local Keypointsmentioning

confidence: 99%

See 1 more Smart Citation

Natural Scene Image Annotation Using Local Semantic Concepts and Spatial Bag of Visual Words

Alqasrawi

2016

SWCC

Self Cite

View full text Add to dashboard Cite

The use of bag of visual words (BOW) model for modelling images based on local invariant features computed at interest point locations has become a standard choice for many computer vision tasks. Visual vocabularies generated from image feature vectors are expected to produce visual words that are discriminative to improve the performance of image annotation systems. Most techniques that adopt the BOW model in annotating images declined favorable information that can be mined from image categories to build discriminative visual vocabularies. To this end, this paper introduces a detailed framework for automatically annotating natural scene images with local semantic labels from a predefined vocabulary. The framework is based on a hypothesis that assumes that, in natural scenes, intermediate semantic concepts are correlated with the local keypoints. Based on this hypothesis, image regions can be efficiently represented by BOW model and using a machine learning approach, such as SVM, to label image regions with semantic annotations. Another objective of this paper is to address the implications of generating visual vocabularies from image halves, instead of producing them from the whole image, on the performance of annotating image regions with semantic labels. All BOW-based approaches as well as baseline methods have been extensively evaluated on 6categories dataset of natural scenes using the SVM and KNN classifiers. The reported results have shown the plausibility of using the BOW model to represent the semantic information of image regions and thus to automatically annotate image regions with labels.

show abstract

Section: Local Semantic Concepts Vs Local Keypointsmentioning

confidence: 99%

“…This paper proposes a framework for automatically annotating image regions with semantic keywords from a predefined vocabulary of words. The framework utilizes a hypothesis presented in [22]. The hypothesis claims that local semantic concepts in natural scenes correlates with local keypoints found in image regions.…”

Section: Introductionmentioning

confidence: 99%

Natural Scene Image Annotation Using Local Semantic Concepts and Spatial Bag of Visual Words

Alqasrawi

2016

SWCC

Self Cite

View full text Add to dashboard Cite

show abstract

Investigating the relationship between the distribution of local semantic concepts and local keypoints for image annotation

Cited by 1 publication

References 9 publications

Natural Scene Image Annotation Using Local Semantic Concepts and Spatial Bag of Visual Words

Natural Scene Image Annotation Using Local Semantic Concepts and Spatial Bag of Visual Words

Contact Info

Product

Resources

About