Whitepaper - LTU Image Technologies

1. Introduction

JASTEC France delivers image recognition technologies via his product LTU Engine. The solution is available via licensed software or via the hosted platform: LTU Engine OnPremise/OnDemand.

LTU Engine includes two distinct images processing technologies:

  • The Visual Search that is divided into two recognition solutions – the image matching and the visual similarity search.
  • The Image Processing that offers a Fine Images Comparison solution.


These principal functionalities are packaged in LTU Engine, which provides the components necessary for creating and managing visual search applications, including JSON API and a comprehensive Administrative Interface.

LTU Engine is fully optimized and let you index a collection of millions of images in a private database, store on a standard server/computer and run all kind of queries on it in the twinkling of an eye.

In this document, you are given details about:

2. Visual Search Solutions

The visual search solutions allow to find, from a query image, identical or similar visuals in images databases. The search is based on object recognition, shape or color and depends on upon the content of an image, rather than on textual information.



The visual search is composed by two key steps:

  • Indexation: The first step toward making an image searchable is to create a descriptor of the image content. LTU Engine computes a visual signature for every image that describe its visual content in terms of color, shape, texture and many higher order visual features and stores it in a reference database. These descriptors are also called image DNAs.
  • Retrivial: A special comparison technology by which an image signature can be compared at extremely high speed with other image signatures from a database up to millions of images.

Each search returns a references list, their distance (or score), optional keywords as well as additional algorithm details.

The distance is an indicator for the relevance of the retrieved images: the closer the value to 0.0, the closer the retrieved image shares the same visual content as the query image.


The visual distance is normalized such that a value:

  • equal to 0 is a clone
  • below 1.0 indicates a match
  • between 1.0 and 1.8 reveals a similarity


2.3.1. Overview

The image matching technology is used to find, in database(s), images that:

  • Look exactly the same (e.g. for deduplication)
  • Have been edited in any way (e.g. for tracking on copyright images)
  • Are photos taken of the same visual content (e.g. for print to mobile applications)


2.3.2. Image Transformations

LTU Engine’s image matching technology is robust against several types of image transformations, detecting not only the exact same image, but also modified versions of the original image and object matches (photographs of same object).




This part illustrates the types of image transformations that LTU Engine can handle in order to identify a match. Image transformations can be broadly divided into several groups:

Often images may be modified with a combination of the above transformations. However, the LTU image matching technology is robust even in those instances. The matching technology easily matched the above combination which includes Gray scale, blur, re-encoding, projective transformation and overlay composite transformations.


2.3.2.1. Geometric Transformations

LTU Engine is capable of identifying image matches despite geometric distortions.

  • Resizing of the original image


  • Arbitrary Rotations


  • Projective distortions

LTU Engine matching technology is capable of handling some degrees of perspective distortions.


2.3.2.2. Photometric Transformations

LTU Engine’s Image matching technology can detect matching images regardless of these photometric transformations:

  • Grayscale: Color image converted to shades of gray.
  • Brightness: Luminance settings correspond to the degree of luminance within each image pixel. For a distant observer, the word ‘luminance’ is substituted by the word ‘brightness’, which corresponds to the sparkling parts of an object or image.
  • Contrast: The difference between the darkest and the brightest parts of an image.
  • Color change (Hue): Changes in coloration; hue is a complex color obtained by a mix of basic colors – Red, Blue, Green.



2.3.2.3. Image Filtering and Noise

Filtering effects are mainly linked with image printing, but also with modifying image metadata. Filtering transformations affect the image 'clarity'. Depending on the filter used, they can either sharpen or blur the image. LTU Engine's image matching technology processes these images without difficulty.


2.3.2.4. Structural Transformations

Structural transformations relate to changes that affect the structure of the image. These transformations do not limit the matching of images using LTU Engine’s image matching technology.

Framed, flipped, text added, cropped
Also, LTU Engine matching technology is capable of handling:

  • Addition of a border or frame: A border of uniform color is added on one, several, or all sides of the image.
  • Flip: Using a particular configuration of LTU Engine’s image matching signature optimized for image tracking applications, the technology is capable of matching flipped images.
  • Addition of text to the image/superimposition: The addition of text to the image with or without a background. With LTU Engine’s technology images are matched regardless of the addition of text.
  • Cropped Images: To cut out or trim unneeded portions of an image or a page. Image matching from LTU Engine handles cropped images without difficulty.



Composite Images

A composite image contains several photographs or graphics in one image and often has a modified background or added text. For this kind of transformation, LTU Engine’s image matching technology delivers extremely accurate results.


2.3.2.5. Compression and Image Encoding

In addition to the visually apparent image transformations detailed above, LTU engine is capable of detecting image clones even if the format or compression of the image has changed. Different image file formats include .bmp, .gif, .jpeg, .pcx, .png, .rsb, .tga, .tif.

Images are often saved in compressed file formats in order to facilitate faster downloading on the Internet. That compression alters the image slightly, but does not typically impact LTU engine’s ability to identify a match.


2.3.2.6. Images Derived from Mobile Devices

The image matching technology from LTU Engine has been optimized to handle query images taken with a mobile device. Due to induced scale changes, motion blur, compression artifacts and usually low quality optics, queries from mobile devices can be challenging to match. JASTEC France has developed an image matching DNA that is particularly robust against combinations of these types of transformations. It is recommended, however, to avoid extensive glare, deep angled shots, very dark lighting and to frame the object of interest accordingly.



2.3.3. Matching Zone

In addition to visual distance, LTU Engine is able to return rich information for a query. For example, LTU Engine can return for each result image, the zones that have matched. This feature is useful:

  • to get visual feedback on the algorithm behavior
  • to implement custom filtering heuristic (do not return result if the matching zone is too small)


2.3.4. Limitations

2.3.4.1. Structure modification too important

The examples below present challenges when matching, due to strong cropping with little structure or advanced composite images.


2.3.4.2. Repetive patern

Because repetive paterns are realy similar, pictures parts could be confused or badly identified.

2.3.4.3. False Positives

The rate of the false positives depends on the application the image matching technology is integrated with.

Since image matching is very prone to detecting small common parts in images such as logos, it sometimes can result in false positives as seen below because parts of the images indeed did match.


Sometimes image matching signature will detect the same object or scene, but not the same image. According to traditional image recognition research terminology these instances would be classified as false positives. However, these types of false positives are desirable when performing very fine similarity searches and when the objective is to match photographs taken of objects – proven especially relevant to mobile applications.


2.3.4.4. Indexing Limitations

Sometimes an image may not be indexed. This is due either to an unknown image format or due to missing image information, i.e. uniform colored images are rejected. Images containing very little information, i.e. having no distinct image features may be rejected too such as this image below.

Finally images with dimensions less than 64×64 pixels are rejected in the default value of the LTU engine (the default setting can be changed).

2.3.4.5. Text Disregard

If the query image contains text, i.e. the scan of a magazine, a screenshot or a sign, false positives may occur for local matching. However, an optional pre-filtering step can be applied to disregard the textual part of the image, which will result in a decreased false positive rate. For instance, if the query image is a scan of a magazine page, the pre-processing step can be applied to extract only the image of interest.

2.4.1. Overview

We provides a solution for finding similar images. By submitting a query image, our technology can find visually similar images.
Similarity can focus on the shapes within the image, its color, or both to:

  • recommend similar products for e-Commerce
  • navigate through a catalog of images
  • return many results, useful for investigation cases




We recommended the advanced signature 4 for similarity search. It analyzes two characteristics: shapes and colors. These parts are independent and their scores are only merged at the end into the final score of the signature.

  • Shape:

Shape similarity is very powerful and can find images regarding different levels of similarity. Algorithm can find images with overall similar shapes. That means if the query image looks like a ball, we will be able to retrieve other images whose overall shape is a ball as well.

  • Texture:

On a finer level, the algorithm is able to detect the kind of texture used in the image. As a result, it finds paintings from the same painter to be similar, if the painter used the same texture techniques on different paintings.

  • Color:

The color part of the signature 4 is invariant to scale, rotation or any linear transformation. Color search with signature 4 is quite flexible and can find images sharing the same colors. It also takes proportion of colors into account.

The relative importance of the color can be set at each query with a color weight.

  • Color Weight 0: If the color weight is zero, then the algorithm will only focus on the similar shapes.
  • Color Weight 100: With a color weight at 100,the algorithm will only take colors into account when looking for similar images.
  • Color Weight 50: An intermediate value between zero and one hundred indicates that both shapes and colors should be taken into account.

2.4.2. Limitations


The shape part of the signature is currently sensible to scale and rotation. Subsequent versions are expected to be invariant to scale.

These images illustrate just one example of the retrieval results possible with similarity search:

2.5.1. Overview

Additionally to image matching, LTU Engine provides LTU Color Query.

LTU Engine Color Query is a powerful tool that analyzes the colors in an image. That allows to:

  • search for images by color(s) with optional color ponderation (e.g. 25% red, 75% green)
  • identify all colors in an image or collection of images: value and percentage
  • upload an image to find images with similar colors
  • find the most popular color or color palette in a collection of images


Whereas lots of existing color tools that require human annotation of the image collection, LTU Engine Color Query is able to analyze the content of your images and automatically identify the present colors. As the process is fully automatic, it is also very accurate. LTU Engine analyses the color that are actually present in the images not only a rough hue. This accuracy allows to look for very specific color tints in an image collection.

2.5.2. Uniform Background removal

By default, the signature is computed on the whole image. On some specific case, this behavior can be problematic. For instance in eCommerce the articles are often shown on a uniform background. Thus the algorithm considers the background color as the article main color. To tackle this issue, LTU Engine introduces a background removal algorithm that identifies uniform backgrounds and computes the signature only on the foreground image. If no uniform background is detected the signature is computed on the whole image.

Images to the right in these two examples show in blue the detected background:

2.5.3. Queries

Once LTU Engine has indexed an image collection, it is possible to run queries on it. There are four kind of queries: get image colors, query by color, query by image, compute palette

2.5.3.1. Get image Colors

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

Colors returned by “get image colors”:

2.5.3.2. Query by color

With LTU Engine you can search in an image collection using a set of colors. For example, LTU Engine let you run a query by color like “pink” or “pink and green”. Then LTU Engine returns you a list of images that have the desired color(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 color 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 color tints.

Results for a query by color “pink”:

Results for a query by color “pink and green”:

With LTU Engine it is also possible to specify the desired color proportion. For instance you can run like a query like ‘look for images with 50% red and 25% yellow’.
Results for a query by color in varying proportions:

2.5.3.3. Query by image

Once you have found an interesting photo (using a query by color for example) you may want to find similar photos in the collection. That is what query by image is for.

Given an input image, query by image looks for images in your collection that have similar colors.

This feature is useful when:

  • there are too many colors in an image to type them all
  • you do not know a specific color code



2.5.4. Interaction with keywords

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.

Results of a query by color “red with keyword ‘sofa’”:

2.5.5. Compute Palette

LTU Engine Color Query can analyze an image collection and return the most frequent colors. The set of the most frequent colors is what we call a color palette.

The color palettes can be used to :

  • suggest relevant queries to the user. (Queries that have results)
  • provide a quick overview on an image collection

An interesting feature of palette is that they can be computed on any subset of a collection.

For instance subsets can be categories. LTU Engine can compute a palette for the “Women Shirt” category. This will be different from the whole image collection palette. Some colors that are not present in this category will be removed and LTU Engine will introduce color nuances for the most present colors.

These subsets can also be result sets. If they are used to propose queries to the user, this feature can be a powerful tool for query refinement.



3. Fine Comparison of Images

Fine image comparison is a specialized technology especially pertinent to media intelligence applications such as advertising identification.

Fine image comparison is designed

  • To automate the comparison of images which match but which may contain difference
  • To provide additional details on the results of matches. The fine image comparison feature provides visual feedback about matched images including a visual highlight showing where differences are located.



The Fine Image Comparison process generates these elements:

  • score: a score is generated which quantifies the visual distance between the two images
  • visual indicators: two analytical images are generated for each fine comparison effected. These analytical images indicate the zones within the images in which there are variations.


The examples below are typical of the types of images to which Fine Image Comparison is applied:

These two images are identical, except for the pricing details in the lower part of the image. The whitened zones in the image at right indicate the zones in which differences are detected.



The differences between these two images are highlighted in the upper left corner.





In a media intelligence application, Fine Image Comparison is typcially used in conjunction with LTU image matching.

  • Unidentified advertisements are compared with a database of know advertisements.
  • Certain ads are identified as definite matches.
  • Other ads are identified as possible matches, but which need validation (their matching scores may indicate the possibility of variations)
  • Fine Image Comparison is applied to the pairs of possible matches. The score generated by Fine Image Comparison determines whether the possible matches should be classified as definite matches or should be examined in a human validation process.