2 research outputs found
Efficient Multimedia Similarity Measurement Using Similar Elements
Online social networking techniques and large-scale multimedia systems are
developing rapidly, which not only has brought great convenience to our daily
life, but generated, collected, and stored large-scale multimedia data. This
trend has put forward higher requirements and greater challenges on massive
multimedia data retrieval. In this paper, we investigate the problem of image
similarity measurement which is used to lots of applications. At first we
propose the definition of similarity measurement of images and the related
notions. Based on it we present a novel basic method of similarity measurement
named SMIN. To improve the performance of calculation, we propose a novel
indexing structure called SMI Temp Index (SMII for short). Besides, we
establish an index of potential similar visual words off-line to solve to
problem that the index cannot be reused. Experimental evaluations on two real
image datasets demonstrate that our solution outperforms state-of-the-art
method.Comment: 17 pages. arXiv admin note: text overlap with arXiv:1808.0961
Object Detection based Deep Unsupervised Hashing
Recently, similarity-preserving hashing methods have been extensively studied
for large-scale image retrieval. Compared with unsupervised hashing, supervised
hashing methods for labeled data have usually better performance by utilizing
semantic label information. Intuitively, for unlabeled data, it will improve
the performance of unsupervised hashing methods if we can first mine some
supervised semantic 'label information' from unlabeled data and then
incorporate the 'label information' into the training process. Thus, in this
paper, we propose a novel Object Detection based Deep Unsupervised Hashing
method (ODDUH). Specifically, a pre-trained object detection model is utilized
to mining supervised 'label information', which is used to guide the learning
process to generate high-quality hash codes.Extensive experiments on two public
datasets demonstrate that the proposed method outperforms the state-of-the-art
unsupervised hashing methods in the image retrieval task