Content-based Image Retrieval using SURF Feature Point Descriptor and Local Binary Pattern with Color Histogram

Authors

  • Vivek Verma Department of CSE/IT, MITS, Gwalior, India
  • Punit Kumar Johari Department of CSE/IT, MITS, Gwalior, India

DOI:

https://doi.org/10.37591/joosd.v2i2.497

Keywords:

content-based image retrieval (CBIR), speed up robust feature (SURF), convex hull, color histogram, local binary pattern (LBP)

Abstract

An important research issue in multimedia technology is the retrieval of similar objects. Most of the content-based image retrieval (CBIR) uses the low-level features such as color, shape and texture to extract the features from the images. A short time ago, the interest points are used to extract the most relevant images with different view point and transformations. Speed up Robust Feature (SURF) is robust and fast interest points detector/descriptor used in many computer vision applications. In this paper, SURF feature points descriptors are computed and then convex hull is used to make region of interest. The low level information inside region of interest is used with color histogram. A texture feature local binary pattern (LBP) is also combined with color histogram to make better retrieval result.

Cite this Article

Verma V, Johari PK. Content-Based Image Retrieval using SURF Feature Point Descriptor and Local Binary Pattern with Color Histogram. Journal of Open Source Developments. 2015; 2(2): 9–15p.

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Published

2015-08-20

Issue

Vol. 2 No. 2 (2015)

Section

Research Articles

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