Deep Product Quantization for Large-Scale Image Retrieval

Zhai, Qi; Jiang, Mingyan

doi:10.1109/icbda.2019.8713223

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, as discussed in Section 2.2, [7] focuses on using a Deep Neural Network and Product Quantization to compress vectors and conduct an Approximate Nearest Neighbor Search. Therefore, it may be possible to build an index based on this approach.…”

Section: Future Workmentioning

confidence: 99%

“…However, excessive variance remains in each subspace. With the development of neural networks, [7] combined deep neural networks and Product Quantization approaches, utilizing deep learning to divide subspaces to minimize the quantization distortion of each subspace. Reference [8] optimized the deep quantization network by using orthonormal vectors to enhance the quantization performance and reduce the redundancy of the codeword, mitigating significant intra-class variations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decentralized Face Identification with Hierarchical Navigable Small World on Blockchain

Lee,

Chen

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector database that is in charge of vector indexing, searching and associating face embeddings to an identity on the Ethereum blockchain with anonymous hashes. Vector indexing and search processes involve different machine learning algorithms that enable computations to be carried out in a reasonable time with good matching accuracy. Specifically, we compared different approaches and selected the HNSW algorithm. Accordingly, we successfully implemented a prototype of a reliable and privacy-focused decentralized face identification system for areas under government surveillance, such as customs inspection sites. In our measurements, the system could handle 20,000 face vectors easily with high matching accuracy, and the performance could be further improved using more powerful hardware. Finally, we also propose additional methods to further scale up the system to handle millions of face vectors.

show abstract

Section: Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized Face Identification with Hierarchical Navigable Small World on Blockchain

Lee,

Chen

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Recently, some research [1,[35][36][37][38][39][40][41] has proposed deep quantization by combining classical quantization methods with a deep neural network. Different from the above methods using artificial image features, deep quantization realizes the end-to-end learning process from image feature extraction to quantization encoding and also works effectively in some applications.…”

Section: Introductionmentioning

confidence: 99%

Approximate Nearest Neighbor Search Using Enhanced Accumulative Quantization

Cheng

Wang

et al. 2022

Electronics

View full text Add to dashboard Cite

Approximate nearest neighbor (ANN) search is fundamental for fast content-based image retrieval. While vector quantization is one key to performing an effective ANN search, in order to further improve ANN search accuracy, we propose an enhanced accumulative quantization (E-AQ). Based on our former work, we introduced the idea of the quarter point into accumulative quantization (AQ). Instead of finding the nearest centroid, the quarter vector was used to quantize the vector and was computed for each vector according to its nearest centroid and second nearest centroid. Then, the error produced through codebook training and vector quantization was reduced without increasing the number of centroids in each codebook. To evaluate the accuracy to which vectors were approximated by their quantization outputs, we realized an E-AQ-based exhaustive method for ANN search. Experimental results show that our approach gained up to 0.996 and 0.776 Recall@100 with eight size 256 codebooks on SIFT and GIST datasets, respectively, which is at least 1.6% and 4.9% higher than six other state-of-the-art methods. Moreover, based on the experimental results, E-AQ needs fewer codebooks while still providing the same ANN search accuracy.

show abstract