Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization

Understand

Hashing method maps similar data to binary hashcodes with smaller hamming distance, and it has received a broad attention due to its low storage cost and fast retrieval speed.

• However, the existing limitations make the present algorithms difficult to deal with large-scale datasets: (1) discrete constraints are involved in the learning of the hash function; (2) pairwise or triplet similarity is adopted to generate efficient hashcodes, resulting both time and space complexity are greater than O(n^2).
• To address these issues, we propose a novel discrete supervised hash learning framework which can be scalable to large-scale datasets.
• First, the discrete learning procedure is decomposed into a binary classifier learning scheme and binary codes learning scheme, which makes the learning procedure more efficient.