Image Retrieval using Harris Corners and Histogram of Oriented Gradients

@article{KVelmurugan2011ImageRU,
  title={Image Retrieval using Harris Corners and Histogram of Oriented Gradients},
  author={K.Velmurugan and S. Santhosh Baboo},
  journal={International Journal of Computer Applications},
  year={2011},
  volume={24},
  pages={6-10}
}
  • K.VelmuruganS. Baboo
  • Published 30 June 2011
  • Computer Science
  • International Journal of Computer Applications
based image retrieval is the technique to retrieve similar images from a database that are visually similar to a given query image. It is an active and emerging research field in computer vision. In our proposed system, the Interest points based Histogram of Oriented Gradients (HOG) feature descriptor is used to retrieve the relevant images from the database. The dimensionality of the HOG feature vector is reduced by Principle Component analysis (PCA). To improve the retrieval accuracy of the… 
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33 References

Content based image retrieval using interest points and texture features

This article presents methods for content based image retrieval based on texture similarity using interest points and Gabor features, and generates a textural description of images.

Application of Image SIFT Features to the Context of CBIR

Experiments show that the application of Lowe's SIFT feature for CBIR can obtain high recall and high precision in the context of CBIR on the famous image databases ZuBud.

PCA-SIFT: a more distinctive representation for local image descriptors

  • Yan KeR. Sukthankar
  • Computer Science
    Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
  • 2004
This paper examines (and improves upon) the local image descriptor used by SIFT, and demonstrates that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation.

Content-Based Image Retrieval Based on Local Affinely Invariant Regions

This contribution develops a new technique for content-based image retrieval that classify the images based on local invariants that represent the image in a very compact way and allow fast comparison and feature matching with images in the database.

Local Grayvalue Invariants for Image Retrieval

This paper addresses the problem of retrieving images from large image databases with a method based on local grayvalue invariants which are computed at automatically detected interest points and allows for efficient retrieval from a database of more than 1,000 images.

Histograms of oriented gradients for human detection

  • N. DalalB. Triggs
  • Computer Science
    2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • 2005
It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.

Object recognition from local scale-invariant features

  • D. Lowe
  • Computer Science
    Proceedings of the Seventh IEEE International Conference on Computer Vision
  • 1999
Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

Distinctive Image Features from Scale-Invariant Keypoints

  • D. Lowe
  • Computer Science
    International Journal of Computer Vision
  • 2004
This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.

Single color extraction and image query

This approach identifies the regions within images that contain colors from predetermined color sets by searching over a large number of color sets, which allows very fast indexing of the image collection by the color contents of the images.

Content-Based Image Retrieval at the End of the Early Years

The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.