Image retrieval method and device and image representation network training method and device
An image retrieval and characterization technology, applied in the computer field, can solve problems such as the influence of user experience, the increase in calculation and time consumption of image retrieval, and the difficulty of image retrieval technology to meet the real-time requirements, so as to reduce the calculation time and the time consumed by retrieval and computing resources, the effect of improving retrieval efficiency
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[0056] For the image representation network mentioned above, in one implementation manner, it can be trained by means of metric learning. In one embodiment, pairs of image samples of the same type and pairs of image samples of non-types can be constructed as training data, and the image representation network is trained by calculating triplet loss.
[0057] In addition, the inventor considers that, in the image retrieval method disclosed in the above embodiments, the image retrieval is realized by using the binarized feature vector. However, the vector distance obtained by comparing the same position elements with the binarized feature vector is not continuous everywhere, so it cannot help optimize the image representation network during the training process. Further, the inventor found through mathematical demonstration that in a discrete space composed of values -1 and 1, there is the following linear relationship between the Hamming distance and the cosine similarity:
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