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Effective storage and retrieval for large-scale images is made possible based on the binary representation used in hashing. Variable hash code (HC) lengths reflect the swap between retrieval speed and accuracy necessary for creating a hashing framework in practical applications. Considering all this, the present hashing algorithms must train several frameworks for various HC lengths, decreasing hashing flexibility and increasing training time costs. Considering that several HCs of varying lengths may be used to describe a sample, there are helpful correlations that can enhance the efficiency of hashing techniques. Nevertheless, the hashing techniques do not entirely use these connections. We suggest a novel method, Asymmetric Supervised Deep Pairwise Hashing (ASDPH), for discriminative learning and to concurrently train HCs of various lengths to overcome the identified issues. Three pieces of information are obtained in this proposed ASDPH approach from HCs of multiple sizes. The samples' original characteristics and labels are used for hash learning. To validate the proposed module, we evaluated the method's performance on 16, 32, 64, and 128 bits for NUSWIDE, CIFAR-10, and MSCOCO datasets by achieving 2%, 7%, and 12% improved mean average precision than other state-of-the-art methods.
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