Abstract
With the fast growing number of images uploaded every day, efficient content-based image retrieval becomes important. Hashing method, which means representing images in binary codes and using Hamming distance to judge similarity, is widely accepted for its advantage in storage and searching speed. A good binary representation method for images is the determining factor of image retrieval. In this paper, we propose a new deep hashing method for efficient image retrieval. We propose an algorithm to calculate the target hash code which indicates the relationship between images of different contents. Then the target hash code is fed to the deep network for training. Two variants of deep network, DBR and DBR-v3, are proposed for different size and scale of image database. After training, our deep network can produce hash codes with large Hamming distance for images of different contents. Experiments on standard image retrieval benchmarks show that our method outperforms other state-of-the-art methods including unsupervised, supervised, and deep hashing methods.
Highlights
Millions of images are uploaded and stored on the Internet every second with the rapid development of storage technique
Our proposed method is compared with state-of-the-art hashing methods including data-independent method locality-sensitive hashing (LSH) [22], two unsupervised methods spectral hashing (SH) [23] and iterative quantization (ITQ) [26], four supervised methods KSH [24], Minimal loss hashing (MLH) [25], binary reconstructive embedding (BRE) [10], and ITQCCA [26], and convolutional neural network (CNN) based deep hashing method CNNH [18] and its variant CNNH+ [18]
We present a novel end-to-end hash learning network for content-based image retrieval
Summary
Millions of images are uploaded and stored on the Internet every second with the rapid development of storage technique. How to efficiently locate a certain number of content similar images from a large database is a big challenge. Speed and accuracy need to be carefully balanced. This kind of task is content-based image retrieval (CBIR) [1,2,3,4], a technique for retrieving images by automatically derived features such as colour, texture, and shape. There are some applications of CBIR like free-hand sketchbased image retrieval [5] whose query images are abstract and ambiguous sketches. In CBIR, derived features are not easy to store. Searching from millions and even billions of images is very time-consuming
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