The semantic gap in image retrieval refers to the disconnect between how humans perceive and interpret visual content versus how it is represented in computational systems. Humans understand images in terms of meaning, while computers rely on low-level features like color, texture, and shape. This gap arises because computational models struggle to associate these low-level features with high-level concepts. For example, a person recognizes a "beach" scene by understanding elements like water, sand, and sky, but a computer only processes pixel-level patterns that may not fully capture the semantic meaning. Bridging the semantic gap is a central challenge in image retrieval. Techniques like deep learning have advanced the field by learning representations closer to human understanding. For instance, convolutional neural networks (CNNs) can identify objects in images, making search results more relevant to user queries.
What is 'semantic gap' in image retrieval?
- The Definitive Guide to Building RAG Apps with LlamaIndex
- Getting Started with Zilliz Cloud
- Large Language Models (LLMs) 101
- Getting Started with Milvus
- Embedding 101
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What encryption methods are recommended for storing audio files?
When it comes to storing audio files securely, several encryption methods can be considered effective. One of the most c
How does the visual backbone (e.g., CNNs, ViTs) interact with language models in VLMs?
In Vision-Language Models (VLMs), the visual backbone, which typically consists of Convolutional Neural Networks (CNNs)
What is a machine vision inspection system?
A machine vision inspection system is a technology that uses cameras and image processing algorithms to automatically in