  This paper proposes a classification network to image semantic retrieval
(NIST) framework to counter the image retrieval challenge. Our approach
leverages the successful classification network GoogleNet based on
Convolutional Neural Networks to obtain the semantic feature matrix which
contains the serial number of classes and corresponding probabilities. Compared
with traditional image retrieval using feature matching to compute the
similarity between two images, NIST leverages the semantic information to
construct semantic feature matrix and uses the semantic distance algorithm to
compute the similarity. Besides, the fusion strategy can significantly reduce
storage and time consumption due to less classes participating in the last
semantic distance computation. Experiments demonstrate that our NIST framework
produces state-of-the-art results in retrieval experiments on MIRFLICKR-25K
dataset.
