Codedretrieval: Joint Image Compression and Retrieval with Neural Networks

With the explosive increase of image data, the efficiency of both image compression and retrieval becomes unprecedentedly significant. However, these two tasks are usually isolated executed, which waste great computational resources in large-scale image applications. In this work, we propose a joint...

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Bibliographic Details
Main Authors: Liu, Linfeng, Liu, Haojie, Chen, Tong, Shen, Qiu, Ma, Zhan
Format: Conference Proceeding
Language:English
Subjects:
Deep Learning
Image Compression
Image Retrieval
Joint Optimization
Online Access:Request full text
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Summary:With the explosive increase of image data, the efficiency of both image compression and retrieval becomes unprecedentedly significant. However, these two tasks are usually isolated executed, which waste great computational resources in large-scale image applications. In this work, we propose a joint framework called CodedRetrieval, which can find a general feature expression for both compression and retrieval based on neural network. Additionally, a two stage training strategy is designed to achieve better balance between the two distinct tasks. Experimental results show that our method can achieve competitive perfomance on both compression and retrieval comparing to classic methods, while saving great amount of computation time.
ISSN:2642-9357
DOI:10.1109/VCIP47243.2019.8965918