Fast Supervised Discrete Hashing and its Analysis
This work addresses the need for faster and more efficient binary hashing methods for large-scale image, video, and document retrieval, but it is incremental as it builds on existing SDH with simplifications.
The authors tackled the problem of improving supervised discrete hashing for large-scale image retrieval by proposing a simplified model called Fast SDH, which outperformed the conventional SDH method in precision, recall, and computation time on a large-scale database.
In this paper, we propose a learning-based supervised discrete hashing method. Binary hashing is widely used for large-scale image retrieval as well as video and document searches because the compact representation of binary code is essential for data storage and reasonable for query searches using bit-operations. The recently proposed Supervised Discrete Hashing (SDH) efficiently solves mixed-integer programming problems by alternating optimization and the Discrete Cyclic Coordinate descent (DCC) method. We show that the SDH model can be simplified without performance degradation based on some preliminary experiments; we call the approximate model for this the "Fast SDH" (FSDH) model. We analyze the FSDH model and provide a mathematically exact solution for it. In contrast to SDH, our model does not require an alternating optimization algorithm and does not depend on initial values. FSDH is also easier to implement than Iterative Quantization (ITQ). Experimental results involving a large-scale database showed that FSDH outperforms conventional SDH in terms of precision, recall, and computation time.