A gradient descent based similarity refinement method for CBIR systems
Esmat Rashedi, Hossein Nezamabadi–pour, Saeı̈d Saryazdi · 2012
This paper provides a short term learning method in CBIR systems based on similarity refinement method. The weights of the similarity function are optimized using gradient decent method to improve the results of a retrieval session. In the proposed approach, the weights of feature's components as well as the weights of each type of features are adjusted. A proper error function is introduced and minimized using gradient descent method. The results are examined in a public dataset with 20000 color images. The experimental results of 60 topic images and comparing with a state-of-the-art method confirm the effectiveness of the proposed method.