2 research outputs found
Multiple Discrimination and Pairwise CNN for View-based 3D Object Retrieval
With the rapid development and wide application of computer, camera device,
network and hardware technology, 3D object (or model) retrieval has attracted
widespread attention and it has become a hot research topic in the computer
vision domain. Deep learning features already available in 3D object retrieval
have been proven to be better than the retrieval performance of hand-crafted
features. However, most existing networks do not take into account the impact
of multi-view image selection on network training, and the use of contrastive
loss alone only forcing the same-class samples to be as close as possible. In
this work, a novel solution named Multi-view Discrimination and Pairwise CNN
(MDPCNN) for 3D object retrieval is proposed to tackle these issues. It can
simultaneously input of multiple batches and multiple views by adding the Slice
layer and the Concat layer. Furthermore, a highly discriminative network is
obtained by training samples that are not easy to be classified by clustering.
Lastly, we deploy the contrastive-center loss and contrastive loss as the
optimization objective that has better intra-class compactness and inter-class
separability. Large-scale experiments show that the proposed MDPCNN can achieve
a significant performance over the state-of-the-art algorithms in 3D object
retrieval
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