AI visual search: finding images with natural language

By teamnext Editorial Team

In the field of artificial intelligence, new language models such as GPT or Gemini are currently driving significant change. In professional media management, the DAM space, this opens up capabilities that previously seemed hard to achieve in practice. One example is visual search using natural language. Images and videos can be searched by what they show, even when no metadata is available.

This article explains the technical foundations and the practical benefits of AI based visual search. There is no established German term yet. In English, the common label is AI visual search. For brevity, the term visual search is used throughout. The broader technical umbrella term is neural search because the technology relies on trained neural networks. Before that, it is important to clarify what natural language means in this context.

What is natural language

Natural language is human language in spoken and written form. Fully developed sign languages also qualify. In this context, the written form is what matters. Spoken language and sign can be captured, but for machine processing the information must be converted into encoded text.

In practice, visual search allows users to enter words, word combinations, full sentences, or sentence fragments to find images. No special rules are required beyond everyday language use. Queries can be very specific, for example:

  • Photo of an older man wearing a sun hat, sitting in a rowing boat and fishing

If no results appear, less important criteria should be removed step by step, for example:

  • An older man sits in a boat and fishes

Capitalisation rules are not relevant. Word order is usually flexible as well, as long as meaning stays intact. The sentences A man is fishing at the lake and At the lake, a man is fishing should therefore lead to comparable results.

Visual search also works in less widely used languages, though not always with the same precision. From a technical perspective, implementations can support more than one hundred languages, from Afrikaans to Zulu.

Visual search uses large language models, or LLMs, to analyse images in a new way, including individual frames from videos. Training the underlying neural networks typically requires very large datasets of image text pairs, often in the range of hundreds of millions.

The goal is to capture semantic relationships between what is shown in an image and associated text such as captions or tags, and to store this information in vector form. Image and text data from each pair are mapped into a shared vector space. During training, semantic proximity between matching image and text pairs is strengthened. The resulting model can generate suitable descriptions for new images, even when specific objects were not explicitly included as training examples.

A visual search system built this way can recognise many everyday objects, including well known products and brands, with high reliability. Text inside images, videos, or documents can also be detected. Manually labelled training sets are mainly required when highly specific objects need to be identified.

The conclusion of this section is that image content can be found reliably through natural language text input without requiring metadata or additional training. Framing this as a game changer is too promotional for an editorial text and should be expressed in more neutral terms.

Three benefits of visual search in the DAM space

For Digital Asset Management, three benefits stand out:

  • Higher efficiency
    If content can be found through visual search, the need for manual tagging and categorisation decreases. Workflows accelerate through automated analysis and classification.

  • Improved findability
    Images and videos remain discoverable even when little or no metadata exists. Queries can be highly specific without relying on manually maintained fields.

  • Accessibility
    Users do not need specialised technical knowledge because queries can be phrased in everyday language. This lowers entry barriers and expands the range of people who can use a DAM system effectively.

Visual search can also improve collaboration because required assets can be provided faster and with greater precision.

Visual search will not replace metadata based search in every scenario. In some sectors, metadata remains essential due to legal requirements or industry standards. In historical archives, research institutions, museums, or specialised stock agencies, validated metadata is likely to remain necessary. Some content can only be described and classified correctly with domain expertise.

For content that does not require academic or specialist knowledge to describe, AI driven enrichment can cover a large part of the work. In practice, organisations can search hierarchical metadata and AI generated vector data, which has no hierarchy, in parallel. This increases flexibility and improves overall findability.

Solutions that combine traditional metadata structures with AI based search capabilities are therefore becoming increasingly common in the DAM market.

Use cases

Embedded in a DAM system, visual search can improve comfort and efficiency across industries. Examples include:

  • Professional sports
    Match situations and emotional moments can be found after an event by entering an action description. Example:

    • Football players in red jerseys celebrate after scoring a goal

  • Marketing and advertising
    Campaign visuals can be found faster when emotions and scenarios are expressed directly in the query. Example:

    • A young woman lies on a green meadow and looks up at the sky with a slight smile

  • E commerce
    In fashion, customers could search by visual product features. Example:

    • Green leather boots for women with a zipper

Closing perspective

AI based visual search is changing the DAM market in noticeable ways. Automated enrichment can significantly reduce the effort required for manual tagging in many organisations. In sectors that rely on validated metadata, combined approaches will still be relevant and can also save time.

At the same time, DAM systems become easier to use when assets can be found via natural language. This benefits user groups without deep technical know how.