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    Visual saliency significantly enhances content-based image retrieval (CBIR) by focusing on human attention. A novel two-stream CNN model integrates saliency for more relevant image search results.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Image Processing

    Background:

    • Content-based image retrieval (CBIR) faces challenges in understanding human query intent and semantic relevance.
    • Visual saliency, reflecting human visual attention, is a promising cue for improving CBIR accuracy.

    Purpose of the Study:

    • To investigate the impact of visual saliency on CBIR performance.
    • To develop a novel deep learning model that incorporates visual saliency for enhanced image retrieval.

    Main Methods:

    • Generated ground-truth saliency maps using eye-tracking data on a CBIR dataset.
    • Developed a two-stream attentive Convolutional Neural Network (CNN) with an auxiliary stream for saliency guidance.
    • Proposed the Main and Auxiliary CNNs (MAC) model to fuse features from both streams.

    Main Results:

    • Visual saliency was found to be beneficial for CBIR tasks.
    • The optimal method for incorporating saliency cues varied across different image retrieval models.
    • The proposed MAC model achieved impressive performance in image retrieval across four public datasets.

    Conclusions:

    • Visual saliency is a crucial factor for improving the semantic relevance in CBIR.
    • The proposed two-stream attentive CNN effectively leverages visual saliency for more human-like image retrieval.
    • The MAC model offers a promising approach for future CBIR systems.