2 research outputs found
Auto-Encoding Twin-Bottleneck Hashing
Conventional unsupervised hashing methods usually take advantage of
similarity graphs, which are either pre-computed in the high-dimensional space
or obtained from random anchor points. On the one hand, existing methods
uncouple the procedures of hash function learning and graph construction. On
the other hand, graphs empirically built upon original data could introduce
biased prior knowledge of data relevance, leading to sub-optimal retrieval
performance. In this paper, we tackle the above problems by proposing an
efficient and adaptive code-driven graph, which is updated by decoding in the
context of an auto-encoder. Specifically, we introduce into our framework twin
bottlenecks (i.e., latent variables) that exchange crucial information
collaboratively. One bottleneck (i.e., binary codes) conveys the high-level
intrinsic data structure captured by the code-driven graph to the other (i.e.,
continuous variables for low-level detail information), which in turn
propagates the updated network feedback for the encoder to learn more
discriminative binary codes. The auto-encoding learning objective literally
rewards the code-driven graph to learn an optimal encoder. Moreover, the
proposed model can be simply optimized by gradient descent without violating
the binary constraints. Experiments on benchmarked datasets clearly show the
superiority of our framework over the state-of-the-art hashing methods. Our
source code can be found at https://github.com/ymcidence/TBH.Comment: CVPR 2020 Accepted, Code at https://github.com/ymcidence/TB
A Decade Survey of Content Based Image Retrieval using Deep Learning
The content based image retrieval aims to find the similar images from a
large scale dataset against a query image. Generally, the similarity between
the representative features of the query image and dataset images is used to
rank the images for retrieval. In early days, various hand designed feature
descriptors have been investigated based on the visual cues such as color,
texture, shape, etc. that represent the images. However, the deep learning has
emerged as a dominating alternative of hand-designed feature engineering from a
decade. It learns the features automatically from the data. This paper presents
a comprehensive survey of deep learning based developments in the past decade
for content based image retrieval. The categorization of existing
state-of-the-art methods from different perspectives is also performed for
greater understanding of the progress. The taxonomy used in this survey covers
different supervision, different networks, different descriptor type and
different retrieval type. A performance analysis is also performed using the
state-of-the-art methods. The insights are also presented for the benefit of
the researchers to observe the progress and to make the best choices. The
survey presented in this paper will help in further research progress in image
retrieval using deep learning