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Encoding

Encoding

Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...

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Facilitating Image Search With a Scalable and Compact Semantic Mapping.

Meng Wang, Weisheng Li, Dong Liu

IEEE Transactions on Cybernetics

|September 24, 2014

Summary

This study presents a new method for efficient image search using semantic embedding. It maps concepts and images into a shared space, improving search accuracy and scalability for dynamic collections.

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Area of Science:

Computer Science
Information Retrieval
Artificial Intelligence

Background:

Image search often suffers from the semantic gap between low-level image features and high-level human concepts.
Existing methods struggle with scalability and efficiently incorporating new semantic information.

Purpose of the Study:

To develop a novel approach for image search using a unified latent semantic space.
To enable efficient and scalable image retrieval by bridging the semantic gap.

Main Methods:

A method to map concepts and image contents into a unified semantic space using semantic concept prototypes.
Learning a linear embedding matrix to map images into the semantic space, ensuring proximity to relevant prototypes.
An efficient update mechanism for incorporating new semantic concept prototypes, enhancing scalability.

Main Results:

The proposed approach effectively narrows the semantic gap in image search.
Demonstrated superior effectiveness, efficiency, and scalability across cross-modality image search tasks on benchmark datasets.
The method supports efficient image search processes in dynamic repositories.

Conclusions:

The novel semantic embedding approach significantly enhances image search capabilities.
The method offers a scalable and efficient solution for cross-modality image retrieval.
This work contributes to more intuitive and effective content-based image retrieval systems.