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Related Concept Videos

Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
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Related Experiment Video

Updated: Apr 4, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Web Image Re-Ranking UsingQuery-Specific Semantic Signatures.

Xiaogang Wang, Shi Qiu, Ke Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new image re-ranking method that learns keyword-specific semantic spaces. This approach significantly enhances search accuracy and efficiency by better matching visual features to user intent.

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    Last Updated: Apr 4, 2026

    Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
    09:20

    Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

    Published on: February 23, 2019

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Information Retrieval

    Background:

    • Image re-ranking improves web image search by refining results based on user-selected query images.
    • Current methods struggle as visual feature similarity often doesn't align with semantic meaning, hindering accurate user intent interpretation.
    • Existing semantic space approaches face challenges in learning universal spaces for diverse web images efficiently.

    Purpose of the Study:

    • To propose a novel image re-ranking framework that addresses the limitations of universal semantic spaces.
    • To develop a method for automatically learning query-specific semantic spaces offline.
    • To improve the accuracy and efficiency of image re-ranking by aligning visual features with semantic intent.

    Main Methods:

    • Offline learning of distinct semantic spaces tailored for different query keywords.
    • Projecting image visual features into query-specific semantic spaces to generate compact semantic signatures.
    • Online re-ranking of images by comparing their semantic signatures within the relevant semantic space.

    Main Results:

    • Achieved significant improvements in re-ranking accuracy and efficiency.
    • Reduced high-dimensional visual features to concise semantic signatures (e.g., 25 dimensions).
    • Demonstrated a 25-40% relative improvement in re-ranking precision compared to state-of-the-art methods.

    Conclusions:

    • The proposed query-specific semantic signature approach effectively bridges the gap between visual features and semantic meaning.
    • This framework offers a more accurate and efficient solution for web-based image re-ranking.
    • The method shows strong potential for enhancing user experience in image search applications.