Flower Image Retrieval Using LBP, Wavelet Moments, Gabor Wavelet Features and Multiple Distance Functions

  • Zannatul Azme
  • Khadiza Sultana Happy
  • Mohammad Farhad Bulbul


Flower image retrieval is a significant and challenging problem in content-based image retrieval. In this paper, we propose a content-based method for retrieving flower images of specified species from a database of flower images of various species. Firstly, we use wavelet moment, Gabor wavelet and Local Binary Pattern (LBP) independently to characterize all flower images in the database. Secondly, we represent a query flower image with Gabor wavelet, wavelet moment, and LBP individually and search images in the database analogous to the query image. The retrieval is accomplished through calculating similarities between the query image and the database images by employing a set of distance functions. Experimental evaluation of the approach reveals that the Gabor wavelet achieves superiority over the wavelet moment and LBP considerably. It is also indicated that the retrieval outcome can be improved through concatenating the Gabor wavelet, wavelet moment, and LBP features rather than utilizing them individually.