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
- Optimizing Your RAG Applications: Strategies and Methods
- The Definitive Guide to Building RAG Apps with LangChain
- GenAI Ecosystem
- Getting Started with Zilliz Cloud
- 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 is the learning rate in the context of deep learning?
The learning rate is a crucial hyperparameter in the training process of deep learning models, such as neural networks.
Manus vs GenSpark
Manus and GenSpark are often compared because both position themselves as systems that go beyond simple question answeri
What is the role of open-source in containerization?
Open-source plays a crucial role in the world of containerization by providing tools, frameworks, and projects that are