Content-based image retrieval (CBIR) is a major challenge in the field of pattern recognition. The CBIR systems attempt to bridge the semantic gap by employing relevance feedback (RF) methods like short term learning (STL). This paper proposes a similarity refinement based STL method in CBIR systems. In this method, the weights of the feature’s components and also the weights of each type of features are optimized by minimizing an error function. The proposed method is examined in a standard public dataset with 10000 color images. The proposed error function improves the precision and computational time in comparison with the similar methods. The experimental results and comparison with the competing methods confirms the effectiveness and efficiency of the proposed method.
Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2013). A Gradient Descent based Short Term Learning Method in CBIR Systems. Journal of Machine Vision and Image Processing, 1(1), 19-27.
MLA
Esmat Rashedi; Hossein Nezamabadi-pour; Saeid Saryazdi. "A Gradient Descent based Short Term Learning Method in CBIR Systems". Journal of Machine Vision and Image Processing, 1, 1, 2013, 19-27.
HARVARD
Rashedi, E., Nezamabadi-pour, H., Saryazdi, S. (2013). 'A Gradient Descent based Short Term Learning Method in CBIR Systems', Journal of Machine Vision and Image Processing, 1(1), pp. 19-27.
VANCOUVER
Rashedi, E., Nezamabadi-pour, H., Saryazdi, S. A Gradient Descent based Short Term Learning Method in CBIR Systems. Journal of Machine Vision and Image Processing, 2013; 1(1): 19-27.
)