Multiple Code Hashing for Efficient Image Retrieval

• URL: http://arxiv.org/abs/2008.01503v2
• Date: Sun, 5 May 2024 16:37:05 GMT
• Title: Multiple Code Hashing for Efficient Image Retrieval
• Authors: Ming-Wei Li, Qing-Yuan Jiang, Wu-Jun Li,
• Abstract summary: We propose a novel hashing framework, called multiple code hashing (MCH) to improve the performance of hash bucket search. MCH is to learn multiple hash codes for each image, with each code representing a different region of the image. To the best of our knowledge, this is the first work that proposes to learn multiple hash codes for each image in image retrieval.
• Score: 16.750400008178293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to its low storage cost and fast query speed, hashing has been widely used in large-scale image retrieval tasks. Hash bucket search returns data points within a given Hamming radius to each query, which can enable search at a constant or sub-linear time cost. However, existing hashing methods cannot achieve satisfactory retrieval performance for hash bucket search in complex scenarios, since they learn only one hash code for each image. More specifically, by using one hash code to represent one image, existing methods might fail to put similar image pairs to the buckets with a small Hamming distance to the query when the semantic information of images is complex. As a result, a large number of hash buckets need to be visited for retrieving similar images, based on the learned codes. This will deteriorate the efficiency of hash bucket search. In this paper, we propose a novel hashing framework, called multiple code hashing (MCH), to improve the performance of hash bucket search. The main idea of MCH is to learn multiple hash codes for each image, with each code representing a different region of the image. Furthermore, we propose a deep reinforcement learning algorithm to learn the parameters in MCH. To the best of our knowledge, this is the first work that proposes to learn multiple hash codes for each image in image retrieval. Experiments demonstrate that MCH can achieve a significant improvement in hash bucket search, compared with existing methods that learn only one hash code for each image.

Related papers

• ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery [128.30514851911218]
  ConceptHash is a novel method that achieves sub-code level interpretability. In ConceptHash, each sub-code corresponds to a human-understandable concept, such as an object part. We incorporate language guidance to ensure that the learned hash codes are distinguishable within fine-grained object classes.
  arXiv  Detail & Related papers  (2024-06-12T17:49:26Z)
• ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch [0.9167082845109439]
  ElasticHash is a novel approach for high-quality, efficient, and large-scale semantic image similarity search. It is based on a deep hashing model to learn hash codes for fine-grained image similarity search in natural images.
  arXiv  Detail & Related papers  (2023-05-08T13:50:47Z)
• Unified Functional Hashing in Automatic Machine Learning [58.77232199682271]
  We show that large efficiency gains can be obtained by employing a fast unified functional hash. Our hash is "functional" in that it identifies equivalent candidates even if they were represented or coded differently. We show dramatic improvements on multiple AutoML domains, including neural architecture search and algorithm discovery.
  arXiv  Detail & Related papers  (2023-02-10T18:50:37Z)
• Asymmetric Hash Code Learning for Remote Sensing Image Retrieval [22.91678927865952]
  We propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for remote sensing image retrieval. The AHCL generates the hash codes of query and database images in an asymmetric way. The experimental results on three public datasets demonstrate that the proposed method outperforms symmetric methods in terms of retrieval accuracy and efficiency.
  arXiv  Detail & Related papers  (2022-01-15T07:00:38Z)
• PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval [68.05570413133462]
  We propose a Pyramid Hybrid Pooling Quantization (PHPQ) module to capture and preserve fine-grained semantic information from multi-level features. Experiments on two widely-used public benchmarks, CUB-200-2011 and Stanford Dogs, demonstrate that PHPQ outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-09-11T07:21:02Z)
• Unsupervised Multi-Index Semantic Hashing [23.169142004594434]
  We propose an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing. We experimentally compare MISH to state-of-the-art semantic hashing baselines in the task of document similarity search. We find that even though multi-index hashing also improves the efficiency of the baselines compared to a linear scan, they are still upwards of 33% slower than MISH.
  arXiv  Detail & Related papers  (2021-03-26T13:33:48Z)
• Deep Reinforcement Learning with Label Embedding Reward for Supervised Image Hashing [85.84690941656528]
  We introduce a novel decision-making approach for deep supervised hashing. We learn a deep Q-network with a novel label embedding reward defined by Bose-Chaudhuri-Hocquenghem codes. Our approach outperforms state-of-the-art supervised hashing methods under various code lengths.
  arXiv  Detail & Related papers  (2020-08-10T09:17:20Z)
• A survey on deep hashing for image retrieval [7.156209824590489]
  I propose a Shadow Recurrent Hashing(SRH) method as a try to break through the bottleneck of existing hashing methods. Specifically, I devise a CNN architecture to extract the semantic features of images and design a loss function to encourage similar images projected close. Several experiments on dataset CIFAR-10 show the satisfying performance of SRH.
  arXiv  Detail & Related papers  (2020-06-10T03:01:59Z)
• Reinforcing Short-Length Hashing [61.75883795807109]
  Existing methods have poor performance in retrieval using an extremely short-length hash code. In this study, we propose a novel reinforcing short-length hashing (RSLH) In this proposed RSLH, mutual reconstruction between the hash representation and semantic labels is performed to preserve the semantic information. Experiments on three large-scale image benchmarks demonstrate the superior performance of RSLH under various short-length hashing scenarios.
  arXiv  Detail & Related papers  (2020-04-24T02:23:52Z)
• A Survey on Deep Hashing Methods [52.326472103233854]
  Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries. With the development of deep learning, deep hashing methods show more advantages than traditional methods. Deep supervised hashing is categorized into pairwise methods, ranking-based methods, pointwise methods and quantization. Deep unsupervised hashing is categorized into similarity reconstruction-based methods, pseudo-label-based methods and prediction-free self-supervised learning-based methods.
  arXiv  Detail & Related papers  (2020-03-04T08:25:15Z)
• Image Hashing by Minimizing Discrete Component-wise Wasserstein Distance [12.968141477410597]
  Adversarial autoencoders are shown to be able to implicitly learn a robust, locality-preserving hash function that generates balanced and high-quality hash codes. The existing adversarial hashing methods are inefficient to be employed for large-scale image retrieval applications. We propose a new adversarial-autoencoder hashing approach that has a much lower sample requirement and computational cost.
  arXiv  Detail & Related papers  (2020-02-29T00:22:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.