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A Mobile Product Image Searching System Integrating Speeded Up Robust Features and Local Binary Pattern

Affiliations

  • Computer Science Department, Thu Duc Technology College, Ho Chi Minh City, Viet Nam
  • Computer Science Department, University of Pedagogy, Ho Chi Minh City, Viet Nam
  • Computer Science Department, University of Science, VNU - HCM, Viet Nam

Abstract


In this paper, we present a novel method image retrieval model for mobile product image searching system. For feature extraction, a method integrating Speeded Up Robust Features (SURF) and Local Binary Pattern (LBP) is proposed. SURF is invariant to rotation, scaling, translation and have low calculated cost, so SURF is quite suitable for mobile product image search model. However, SURF is not effective with the noisy images, blur images, illuminated images. Because LBP operator is invariant to changes in illumination and contrast of images, we use LBP to supplement for disadvantages of the SURF features. Our proposed method can improve accuracy and speed of the system. For query, a query model using K-NN Search with vector quantization is used. This model improve performance and reduce the cost of computation of the mobile product image searching system. The experimental results show the feasibility of our proposal model.

Keywords

Content-based Image Retrieval (CBIR), Feature Integration, K-Nearest Neighbor (K-NN), Local Binary Pattern (LBP), Mobile Product Image Search, Speeded Up Robust Features (SURF).

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