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Related Concept Videos

Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

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The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Related Experiment Video

Updated: Dec 27, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

Deep Category-Level and Regularized Hashing With Global Semantic Similarity Learning.

Yaxiong Chen, Xiaoqiang Lu

    IEEE Transactions on Cybernetics
    |March 1, 2020
    PubMed
    Summary

    This study introduces Deep Category-level and Regularized Hashing (DCRH), a new deep hashing method for image retrieval. DCRH improves hash code learning by better utilizing global semantic similarity and category-level information.

    Related Experiment Videos

    Last Updated: Dec 27, 2025

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.9K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Information Retrieval

    Background:

    • Hashing techniques are vital for large-scale image retrieval due to efficiency.
    • Existing deep hashing methods often fail to fully capture global semantic similarity and category-level information, leading to information loss.

    Purpose of the Study:

    • To propose a novel deep hashing approach, Deep Category-level and Regularized Hashing (DCRH), to enhance semantic similarity of hash codes.
    • To leverage global semantic similarity and category-level semantic information for improved hash code learning.

    Main Methods:

    • Designed a global semantic similarity constraint for deep features.
    • Utilized label information to enhance category-level semantics in hash codes.
    • Developed a triplet construction module for effective hash function learning.
    • Proposed a triplet regularized loss (Reg-L) term to minimize information loss.

    Main Results:

    • The DCRH approach demonstrated superior performance compared to state-of-the-art methods.
    • Experiments were conducted on three benchmark image retrieval datasets.
    • The method effectively enhances semantic similarity of hash codes.

    Conclusions:

    • DCRH successfully addresses limitations of existing deep hashing methods.
    • The proposed approach offers improved performance in large-scale image retrieval.
    • DCRH provides a robust framework for learning semantically rich hash codes.