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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Deep Neural Networks for Image-Based Dietary Assessment
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Entropy-Optimized Deep Weighted Product Quantization for Image Retrieval.

Lingchen Gu, Ju Liu, Xiaoxi Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 1, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Entropy Optimized deep Weighted Product Quantization (EOWPQ) improves image retrieval by flexibly encoding samples with weighted codewords. This method balances codeword representation and assignment, enhancing overall performance.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep learning significantly enhances large-scale image retrieval through hashing and quantization.
    • Deep product quantization (DPQ) methods are gaining attention, but face challenges in codeword representation and balanced assignment.
    • Current DPQ methods often suffer from imbalanced codeword representation, leading to redundancy or insufficiency and reduced retrieval performance.

    Purpose of the Study:

    • To propose a novel deep product quantization method, Entropy Optimized deep Weighted Product Quantization (EOWPQ), to improve representation capability and balance codeword assignment.
    • To enhance the representation capability of codewords and ensure balanced assignment for better image retrieval.
    • To maintain semantic information within codewords through a flexible encoding approach.

    Main Methods:

    • Encoding samples using a linear weighted sum of codewords, offering a flexible alternative to traditional single codeword assignment.
    • Establishing a linear relationship between weighted codewords and semantic labels to preserve semantic information.
    • Maximizing the entropy of the coding probability distribution using optimal transport theory to achieve balanced codeword assignment and optimize representation capability.

    Main Results:

    • EOWPQ demonstrates superior retrieval performance on three benchmark datasets compared to existing methods.
    • The proposed method shows significant improvements in the representation capability of codewords.
    • EOWPQ effectively balances the assignment of codewords, mitigating issues of redundancy and insufficiency.

    Conclusions:

    • EOWPQ offers a novel and effective approach to deep product quantization for large-scale image retrieval.
    • The method successfully addresses the limitations of traditional DPQ by improving codeword representation and achieving balanced assignment.
    • EOWPQ provides a robust framework for enhancing image retrieval accuracy and efficiency.