Large-scale image retrieval using local binary patterns and iterative quantization
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Authors
Shakerdonyavi, M.
Shanbehzadeh, J.
Sarrafzadeh, Hossein
Shanbehzadeh, J.
Sarrafzadeh, Hossein
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Date
2015-11
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Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
binary codes
content-based retrieval
image retrieval
image feature extraction
large scale content based image retrieval (LSCBIR)
hashing algorithm
content-based retrieval
image retrieval
image feature extraction
large scale content based image retrieval (LSCBIR)
hashing algorithm
ANZSRC Field of Research Code (2020)
Citation
Shakerdonyavi, M., Shanbehzadeh, J., & Sarrafzadeh, A. (2015, November). Large-Scale Image Retrieval Using Local Binary Patterns and Iterative Quantization. - (Ed.), International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp.1-5). 10.1109/DICTA.2015.7371276.
Abstract
Hashing algorithm is an efficient approximate searching algorithm for large-scale image retrieval. Learning binary code is a key step to improve its performance and it is still an ongoing challenge. The inputs of Hashing affects its performance. This paper proposes a method to improve the efficiency of learning binary code by improving the suitableness of the Hashing algorithms inputs by employing local binary patterns in extracting image features. This approach results in more compact code, less memory and computational requirement and higher performance. The reasons behind these achievements are the binary nature and high efficiency in feature generation of local binary pattern. The performance analysis consists of using CIFAR-10 and precision vs. recall rate as dataset and evaluation criteria respectively. The simulations compare the new algorithm with three state of the art and along the line algorithms from three points of view; the hashing code size, memory space and computational cost, and the results demonstrate the effectiveness of the new approach.
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DOI
10.1109/DICTA.2015.7371276
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