Next Article in Journal
Computing Persistent Homology of Directed Flag Complexes
Previous Article in Journal
A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
Previous Article in Special Issue
Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
Open AccessArticle

Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval

1
School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China
2
School of Computer Science and Technology, Jilin University, Changchun 130000, China
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(1), 18; https://doi.org/10.3390/a13010018
Received: 16 December 2019 / Revised: 2 January 2020 / Accepted: 3 January 2020 / Published: 6 January 2020
(This article belongs to the Special Issue Algorithms for Pattern Recognition)
In recent years, binary coding methods have become increasingly popular for tasks of searching approximate nearest neighbors (ANNs). High-dimensional data can be quantized into binary codes to give an efficient similarity approximation via a Hamming distance. However, most of existing schemes consider the importance of each binary bit as the same and treat training samples at different positions equally, which causes many data pairs to share the same Hamming distance and a larger retrieval loss at the top position. To handle these problems, we propose a novel method dubbed by the top-position-sensitive ordinal-relation-preserving bitwise weight (TORBW) method. The core idea is to penalize data points without preserving an ordinal relation at the top position of a ranking list more than those at the bottom and assign different weight values to their binary bits according to the distribution of query data. Specifically, we design an iterative optimization mechanism to simultaneously learn binary codes and bitwise weights, which makes their learning processes related to each other. When the iterative procedure converges, the binary codes and bitwise weights are effectively adapted to each other. To reduce the training complexity, we relax the discrete constraints of both the binary codes and the indicator function. Furthermore, we pretrain a tensor ordinal graph to decrease the time consumption of computing a relative similarity relationship among data points. Experimental results on three large-scale ANN search benchmark datasets, i.e., SIFT1M, GIST1M, and Cifar10, show that the proposed TORBW method can achieve superior performance over state-of-the-art approaches. View Full-Text
Keywords: image retrieval; binary code; hash algorithm; bitwise weights; top-rank-sensitive; ordinal-relation-preserving image retrieval; binary code; hash algorithm; bitwise weights; top-rank-sensitive; ordinal-relation-preserving
Show Figures

Figure 1

MDPI and ACS Style

Wang, Z.; Sun, F.; Zhang, L.; Wang, L.; Liu, P. Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval. Algorithms 2020, 13, 18.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop