Image retrieval is an essential area of computer vision, but it faces several open problems that affect its effectiveness. One major issue is semantic gap. While traditional image retrieval methods rely on visual features like color, texture, and shape, these features don’t always align with human perception or intent. Images with similar content may look very different at the pixel level, leading to mismatches in search results. Closing this semantic gap requires models that can better understand the meaning behind images. Scalability is another challenge, especially with large image datasets. As the amount of visual data grows, maintaining efficient search and retrieval systems becomes more difficult. Indexing high-dimensional feature vectors for millions of images in real-time is computationally expensive, and reducing this overhead while maintaining retrieval quality is a significant hurdle. A related problem is image diversity and context, where retrieval systems struggle to return relevant results when a query is ambiguous or when the context in which an image is used is critical to understanding its meaning. For example, an image of a car might be relevant in the context of an advertisement but not in a search for vehicles for sale. To address this, systems need to incorporate more context-aware techniques and multimodal inputs, such as text or user preferences. Finally, cross-modal retrieval, where queries consist of text or other data types and the goal is to retrieve images, is still an open problem. Improving the alignment between visual features and textual descriptions or queries requires better feature fusion methods and deeper understanding of both modalities.
What are the open problems for image retrieval?
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