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    Colour Extraction

    Colour Extract is a powerful tool that analyses and lists featured colours of your images

    Fast, Robust, and Scalable Technology

    LTU’s vision aims to enhance computer vision to provide with fast and accurate results.

    Whereas lots of existing colour tools that require human annotation of the image collection, LTU Colour Search is able to analyse the content of your images and automatically identify the present colours.



    Colour Search current version : sig. 8


    1 query retrieval is under 1 second for 1 million image index on only 1 CPU core

    Get Colours

    For each image in the collection LTU Colour Search can return you the list of colours that are present in this image. This feature is typically used in combination with query by colour or query by image to print the colours of the query results.

    Query by Colour

    With LTU Colour Search you can search in an image collection using a set of colours. For example, LTU Engine let you run a query by colour like “pink” or “pink and green”. Then LTU Engine returns you a list of images that have the desired colour(s). This list is sorted by relevance. LTU algorithm is very accurate. It is able to look very specific tints. It is also very robust. The algorithm returns the images with the required colour tints at top positions but it also return images with slightly different tints at higher positions (or at top positions if none of the image contains the required colour tints..


    Keywords Assignment

    Keywords can be assigned to each image in the collection. Keywords can then be used to restrict the query result to some specific categories. For instance it is possible to run a query by color “red with keyword sofa”. Keywords are compatible with Query by color and Query by image.

    Ask for a demo

    Enjoy four types of queries: get image colours, query by colour, query by image, compute palette

    Colour Extraction

    Find images featuring the colour of your choice and detect colours in your images for better classification