1 research outputs found
Improved Search in Hamming Space using Deep Multi-Index Hashing
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. There has been considerable
research on generating efficient image representation via the
deep-network-based hashing methods. However, the issue of efficient searching
in the deep representation space remains largely unsolved. To this end, we
propose a simple yet efficient deep-network-based multi-index hashing method
for simultaneously learning the powerful image representation and the efficient
searching. To achieve these two goals, we introduce the multi-index hashing
(MIH) mechanism into the proposed deep architecture, which divides the binary
codes into multiple substrings. Due to the non-uniformly distributed codes will
result in inefficiency searching, we add the two balanced constraints at
feature-level and instance-level, respectively. Extensive evaluations on
several benchmark image retrieval datasets show that the learned balanced
binary codes bring dramatic speedups and achieve comparable performance over
the existing baselines