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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy guided multi level feature fusion network for high precision content based image retrieval.

M Lavanya1, G Vennira Selvi2, R Gopi3

  • 1Faculty of Artificial Intelligence and Data Science, Adhiparasakthi College of Engineering, Ranipet District, Kalavai, 632 506, Tamilnadu, India.

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Summary
This summary is machine-generated.

This study introduces an adaptive framework for content-based image retrieval (CBIR) that enhances accuracy by combining deep and handcrafted features. The new system significantly improves image search performance and reliability for large-scale applications.

Keywords:
Content-based image retrievalDatabase imagesImage enhancementImage segmentationQuery images

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Artificial Intelligence

Background:

  • Traditional content-based image retrieval (CBIR) systems struggle with accuracy due to fixed feature weights and semantic gaps.
  • Heterogeneous visual features are often poorly exploited in existing CBIR frameworks.
  • Managing and searching vast image repositories requires more effective retrieval methods.

Purpose of the Study:

  • To develop a novel multi-feature adaptive CBIR framework.
  • To enhance retrieval accuracy by intelligently fusing deep and handcrafted features.
  • To improve the efficiency and reliability of large-scale image search.

Main Methods:

  • Combines deep convolutional features with handcrafted low-level descriptors.
  • Employs information entropy-based fusion and a trust-based weighting system for adaptive feature integration.
  • Utilizes a PageRank-based similarity propagation strategy for refined image ranking.

Main Results:

  • The proposed framework significantly improves performance across benchmark datasets.
  • Achieved up to 8.6% boost in Mean Average Precision (mAP) compared to traditional methods.
  • Demonstrated significant increases in Precision@10 (6.2%) and NDCG@10 (7.4%) with robust statistical validation.

Conclusions:

  • Entropy-driven adaptive fusion and ranking refinement effectively address limitations of current CBIR systems.
  • The framework offers a robust and reliable solution for practical, large-scale image search.
  • The adaptive approach ensures consistent performance improvements in image retrieval accuracy.