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imagealgorithms:functionalities-new [2019/06/27 17:40]
pdufour [4. Image data extraction]
imagealgorithms:functionalities-new [2019/10/17 14:58] (current)
pdufour [3.1.1. Overview]
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 ====== - Introduction ====== ====== - Introduction ======
  
-LTU delivers visual intelligence for image management and recognition tailored for your usecase.+LTU delivers visual intelligence ​solutions ​for image management and recognition tailored for your usecase.
 \\ \\
-So every case is considered individually. Depending on your need, LTU builds appropriate scenario calling multiple algorithms.\\ 
-You can choose to deploy on your own servers for security or privacy reasons, or use LTU's Cloud if you don’t want to deal with server purchasing and maintenance. 
 \\ \\
-This document explain you each technologies provided and used by LTU. +Every case is considered individually. Depending on your need, LTU builds appropriate scenario calling multiple algorithms.  
 +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.  
 +Each technologies provided and used by LTU is explained in this document 
 +\\ 
 +\\ 
 +The solution is available via licensed software or via the hosted platform. You can choose to deploy on your own servers for security or privacy reasons, or use LTU's Cloud if you don’t want to deal with server purchasing and maintenance.
 \\ \\
 \\ \\
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 \\ \\
  
-====== - Image Recognition ​Technologies ​======+====== - Images management ​ ====== 
 +{{ :​imagealgorithms:​ltu-cl3.png?​nolink&​1200 |}} 
 +\\ 
 +\\ 
 +LTU Engine is a complete solution that will help you to organize and manage your images. All the following solutions could be used to structure and manage your images:  
 +  * Indexation: using to create your private database of searchable images 
 +  * Deeplearning:​ using to classify your images and to get associated keywords 
 +  * OCR: using to get text informations from an image 
 +  * Color algorythm: using to get colors of an image 
 + 
 +Once your images structured, you will be able to search inside by using keywords or/and image search algorythms. 
 + 
 +{{ :​imagealgorithms:​stolen_art2.png?​nolink&​700 |}} 
 +===== - Image indexation ===== 
 + 
 +LTU provides images recognition solutions that allows to find references from a query in a private images database. The search solutions are described in the second part of this whitepaper. 
 +\\ 
 +\\ 
 +{{ :​imagealgorithms:​algo-0094.png?​nolink&​1000 |}} 
 +\\ 
 +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. These descriptors are also called image DNAs. The DNAs are unique for each image and specific to a search algorithm. 
 +\\ 
 +\\ 
 +{{gdraw>​1uaEUDl2BHGWP79LPzNyOMNmWeza7Iv52Zww8Z5QsSTA width=960 title="​reference database title" center}} 
 +\\ 
 + 
 +LTU Engine stores the DNAs in a database that constitutes your images reference database, in which you would make search by using LTU query retriaval solution.  
 +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. 
 +===== - Artificial Inteligence ===== 
 + 
 +LTU uses deep learning models to classify large batches of images, detect objects in an image and generate Keywords. Our computer vision processes aim to provide our clients with bespoke, fast and accurate image recognition. 
 + 
 +LTU recognises retail products, decorated objects, pictures, book covers, textbooks, art paintings, logos, and more 
 +focused on Image processing. 
 +==== - Deep Learning and Transfer Learning ==== 
 + 
 +Deep learning is a type of artificial intelligence derived from machine learning where the machine is able to learn on its own. Deep Learning is based on a network of artificial neurons inspired by the human brain. This network is composed of tens or even hundreds of "​layers"​ of neurons, each receiving and interpreting the information of the previous layer. 
 +\\ 
 +\\ 
 +At each step, the "​bad"​ answers are eliminated and returned to the upstream levels to adjust the mathematical model. As you go along, the program reorganizes the information into more complex blocks. The model is pre-trained on a training dataset. 
 +\\ 
 +\\ 
 +When this model is subsequently applied to other cases, it is normally able to recognize an object without anyone ever telling him that he has never learned the concept of this object. 
 + 
 +<WRAP center round tip 100%> 
 +The starting data is essential: the more different experiences the system accumulates,​ the more efficient it will be. 
 +</​WRAP>​ 
 + 
 +Deep learning, can be optimized thanks to Transfer Learning: the knowledge acquired from the training dataset, called the "​source"​ dataset, is "​transferred"​ in order to properly handle the new dataset, named "​target"​. For example, knowledge gained while learning to recognize cars can be used to some extent to recognize trucks. 
 +Thus, Transfer learning allows us to build accurate models in great time efficiency.  
 +\\ 
 +\\ 
 +Based on the Transfer Learning principles, LTU implements image classification tailored for your specific needs as described bellow.  
 +\\ 
 + 
 + 
 +\\ 
 +=== - Detection and Identification === 
 + 
 +Image Classification is the task of taking an image as input and outputting a class label from a set of classes to which the image belongs. The process normally involves recognition of the dominant content in a scene. The dominant content gets the strongest confidence score irrespective of the transformation of that content such as scaling, location or rotation.  
 + 
 +LTU Engine interface allows you to drag and drop your dataset and annotate each image using your specific terminology.  
 + 
 +| {{ :​imagealgorithms:​classification_ex.png?​nolink&​450 |}} |{{ :​imagealgorithms:​classification2.png?​nolink&​700 |}}| 
 + 
 + 
 + 
 +==== -Keywords ==== 
 + 
 +As explained Deep Learning helps LTU to classify in pre-entrained classes, your images.  
 +LTU also allows to associate these classes in keywords. 
 +Thus Keywords will help to organize your dataset, to search images with keywords inside your dataset and to orient the image search process. 
 + 
 + 
 +{{ :​imagealgorithms:​keywords.png?​nolink&​600 |}} 
 + 
 +===== -Optical Character ​Recognition ​===== 
 + 
 + 
 +Optical character recognition refers to both the technology and process of reading and converting typed, printed or handwritten characters into machine-encoded text or something that the computer can manipulate. 
 + 
 +{{ :​imagealgorithms:​ocr.png?​nolink&​400 |}} 
 +\\ 
 + 
 + 
 +LTU used OCR solutions to extract text from an image. The retrieved information could help to 
 +  * Classify images dataset 
 +  * Determine keywords 
 + 
 +{{ :​imagealgorithms:​combinaison2.png?​nolink&​700 |}} 
 + 
 +===== - Colors palette ===== 
 + 
 +==== - Image colors ==== 
 + 
 +LTU Engine can return you the list of colors that are present in an image.  
 +\\  
 +\\  
 +{{ :​imagealgorithms:​algo-039.jpg?​nolink&​1000 |}} 
 +\\ 
 +\\ 
 +==== - Trend of an images collection ===== 
 +LTU Engine Color can analyze an images 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 
 +  * provide a quick overview on an images 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 images 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. 
 +\\  
 +\\  
 +{{ :​imagealgorithms:​algo-046.jpg?​nolink&​1000 |}} 
 +\\ 
 +\\ 
 + 
 + 
 +====== - Image recognition ​======
  
 {{ :​imagealgorithms:​ltu-re4.png?​nolink&​1200 |}} {{ :​imagealgorithms:​ltu-re4.png?​nolink&​1200 |}}
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 \\  \\ 
   * **[[functionalities#​visual_search_solutions|The Visual Search]]** that is divided into two recognition solutions – the image matching and the visual similarity search.   * **[[functionalities#​visual_search_solutions|The Visual Search]]** that is divided into two recognition solutions – the image matching and the visual similarity search.
-  * **[[functionalities#​fine_comparison_of_images|The Image Processing]]** that offers a Fine Images Comparison solution. +  * **[[functionalities#​fine_comparison_of_images|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 part, you are given details about: ​ 
-  ***[[functionalities#​image_matching_technology|Matching search]]** ​ 
-  ***[[functionalities#​similarity_search|Similarity search]]** 
-  ***[[functionalities#​color_search|Color search]]** 
-  ***[[functionalities#​fine_comparison_of_images|Fine Images Comparison]]** ​ 
 ===== - Visual Search Solutions ===== ===== - Visual Search Solutions =====
  
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 /* {{ :​imagealgorithms:​algo-0092.jpeg |Visual similarity search}} */ /* {{ :​imagealgorithms:​algo-0092.jpeg |Visual similarity search}} */
 \\ \\
 +Our clients used the visual search
 +  * Art Identification:​ to know if an art work is stollen
 +  * Brand Intelligence:​ to survey if a merchandise is not conterfeited
 +  * Media Intelligence:​ to analyse what Internet relates about him or his product
 +  * Place Detection: to find a place from a picture
 +  * Page Identification:​ to get information of a product from an ad in the street
 +  * and more...
 +
 The visual search is composed by two key steps: 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.+  ***Indexation**: ​[[functionalities#​image_indexation|As with the reference images]], the first step is to create a descriptor of the image content. LTU Engine computes a visual signature for every query that describes ​its visual content in terms of color, shape, texture and many higher order visual features. 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.   ***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.
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 \\ \\
-{{ :​imagealgorithms:​distance.jpeg?​nolink&​800 |}}+{{ :​imagealgorithms:​algo-001.jpg?​nolink&​800 |}}
 ==== - Image Matching Technology ==== ==== - Image Matching Technology ====
 === - Overview === === - Overview ===
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   ***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. ​   ***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.   ***Contrast:​** The difference between the darkest and the brightest parts of an image.
-  ***Color change (Hue):​** ​ Changes in colorationhue is a complex color obtained by a mix of basic colors – Red, Blue, Green.+  ***Color change (Hue):​** ​ Changes in colorationhue is a complex color obtained by a mix of basic colors – Red, Blue, Green.
 \\  \\ 
 {{ :​imagealgorithms:​transformation.jpg?​nolink&​800 |}} {{ :​imagealgorithms:​transformation.jpg?​nolink&​800 |}}
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 == - Structural Transformations == == - 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+Changes related ​to structural transformations ​affect the structure of the image. These transformations do not limit the matching of images. ​
 \\  \\ 
 \\ \\
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 === - Limitations === === - Limitations ===
  
-== - Structure modification too important == +== - Too important ​structure modification ​== 
-The examples below present challenges when matching, due to strong cropping with little structure or advanced composite images. ​+The examples below present challenges when matching, due to strong cropping with little structure or too advanced composite images. ​
 \\  \\ 
 \\  \\ 
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 The rate of the false positives depends on the application the image matching technology is integrated with. 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.+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 match.
 \\  \\ 
 \\  \\ 
 {{ :​imagealgorithms:​fauxpositif.jpg?​nolink&​800 |}} {{ :​imagealgorithms:​fauxpositif.jpg?​nolink&​800 |}}
 \\ \\
-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. ​+Sometimes image matching ​algorythm detects ​the same object or scene, but not the same image. According to traditional image recognition ​search ​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. ​
 \\  \\ 
 \\  \\ 
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-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. ​+Sometimes an image may not be indexed. This is due either to an unknown image format or due to missing image information 
 +  * Uniform ​colored images are rejected. ​ 
 +  * Having ​no distinct image features may be rejected too such as this image below. ​
 \\  \\ 
 \\ \\
 {{ :​imagealgorithms:​cafe2.png?​nolink&​400 |}} {{ :​imagealgorithms:​cafe2.png?​nolink&​400 |}}
    
-Finally images with dimensions less than 64x64 pixels are rejected in the default value of the LTU engine ​(the default setting can be changed).+Finally images with dimensions less than 64x64 pixels are rejected in the default value of the LTU Engine ​(the default setting can be changed).
  
 \\ \\
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 ==== - Similarity Search ==== ==== - Similarity Search ====
-=== - Overview ​===+=== - visual search ​===
  
-We provides a solution for finding similar images. By submitting a query image, our technology can find visually similar images.+LTU 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: Similarity can focus on the shapes within the image, its color, or both to:
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 \\ \\
-{{ :​imagealgorithms:​similarity.jpeg?​nolink&​1000 |}} 
-{{ :​imagealgorithms:​similarity2.jpeg?​nolink&​1000 |}} 
 \\ \\
 +{{ :​imagealgorithms:​algo-0092.jpeg?​nolink |}}
 \\ \\
-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. 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.
  
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 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. 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:​**  ​   ***Color:​**  ​
-The color part of the signature ​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 color part of the signature is invariant to scale, rotation or any linear transformation. ​ Color search is quite flexible and can find images sharing the same colors. It also takes proportion of colors into account.
 \\ \\
 \\ \\
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   ***Color Weight 50:**  An intermediate value between zero and one hundred indicates that both shapes and colors should be taken into account.   ***Color Weight 50:**  An intermediate value between zero and one hundred indicates that both shapes and colors should be taken into account.
  
 +\\
  
- +=== - Search by keywords ​=== 
- +LTU solution also allows to search in an images collection by using one or a combination of several keywords. 
- +The similarity ​is not just visual anymore, it is thematic
- +For example, that could help to find all the pictures of a photograph whose theme is "​fruits"​
-==== - Limitations ==== +\\ 
-\\  +\\ 
-The shape part of the signature ​is currently sensible to scale and rotation+{{ :​imagealgorithms:​fruit.png?​nolink&​600 |}}
-Subsequent versions are expected ​to be invariant to scale. +
-\\  +
-\\  +
-These images illustrate just one example of the retrieval results possible with similarity search: +
-\\  +
-\\  +
-{{ :​imagealgorithms:​algo-008.jpg?​nolink&​1000 |}} +
 ==== - Color Search ==== ==== - Color Search ====
 === - Overview === === - Overview ===
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 {{ :​imagealgorithms:​colorsearch.png?​nolink&​800 |}} {{ :​imagealgorithms:​colorsearch.png?​nolink&​800 |}}
  
-LTU Engine Color Query is a powerful tool that analyzes ​the colors in an image. ​That allows to:+LTU Engine Color Query is a powerful tool that analyses ​the colors in an image. ​ 
 +As explained in the part 1 of this document, that allows to 
 +  * find the most popular color or color palette in a collection of images 
 +  * identify all colors in an image or collection of images: value and percentage 
 +But color could also be a criteria of search:
   * search for images by color(s) with optional color ponderation (e.g. 25% red, 75% green)   * 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   * upload an image to find images with similar colors
-  * find the most popular color or color palette in a collection of images 
  
 \\ \\
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-  + \\ 
-==== - Uniform Background removal ​====+=== - 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. 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. ​ 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:  ​ 
 \\  \\ 
 \\  \\ 
 {{ :​imagealgorithms:​algo-038.jpg?​nolink&​1000 |}} {{ :​imagealgorithms:​algo-038.jpg?​nolink&​1000 |}}
  
-  +\\ 
-==== - Queries ​====+=== - Queries ===
  
  
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 \\ \\
 \\ \\
-=== - 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. +== - Query by color ==
- +
- +
-Colors returned by "get image colors":​ +
-\\  +
-\\  +
-{{ :​imagealgorithms:​algo-039.jpg?​nolink&​1000 |}} +
-\\ +
-\\ +
-=== - Query by color ===+
  
 With LTU Engine you can search in an image collection using a set of colors. With LTU Engine you can search in an image collection using a set of colors.
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 \\ \\
 \\ \\
-=== - Query by image ===+== - Query by image ==
  
  
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 ==== - Interaction with keywords ==== ==== - 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.+ As explained in the part 1, keywords ​can be assigned to each image in collection. Keywords ​can be attributed manually or be the result of a deep learning process.\\ 
 +So, keywords ​can then be used with a visual search process ​to restrict the query result to some specific categories. For instance it is possible to run a query with keyword ​"sofa”. Keywords are compatible with Query by color and Query by image.
  
  
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 {{ :​imagealgorithms:​algo-045.jpg?​nolink&​1000 |}} {{ :​imagealgorithms:​algo-045.jpg?​nolink&​1000 |}}
  
-==== - 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.+===== - Link images with Metadata =====
  
-For instance subsets can be categories. ​LTU Engine can compute a palette for the “Women Shirt” categoryThis will be different from the whole image collection paletteSome colors that are not present ​in this category will be removed and LTU Engine ​will introduce color nuances for the most present colors.+LTU offers you to associate metadata to your imagesSo, once an image is recognized you can access to all data you have stored withMetadata ​are saved in a separated database. 
 +{{gdraw>​15UmFRMYwtWyaB72kiybDRj7UD_fuZB6VG133T72BQgA width=940 title="​LTU Engine" center}}
  
-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. 
-\\  
-\\  
-{{ :​imagealgorithms:​algo-046.jpg?​nolink&​1000 |}} 
-\\ 
 \\ \\
 +
 +===== - OCR =====
 +
 +LTU could use OCR algorythm to improve a result given by an image search.
 +
 +{{ :​imagealgorithms:​ocr-ex.png?​nolink&​600 |}}
 +
 +
 +
 +
 +
 +
 +
 +
  
 ===== - Fine Comparison of Images ===== ===== - Fine Comparison of Images =====
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 \\ \\
 \\ \\
-===== - Link images with Metadata ===== 
- 
-LTU offers you to associate metadata to your images. So, once an image is recognized you can access to all data you have stored. Metadata are saved in a separated database. 
-{{gdraw>​15UmFRMYwtWyaB72kiybDRj7UD_fuZB6VG133T72BQgA width=940 title="​LTU Engine"​ center}} 
- 
-====== - Image classification tools for data management ====== 
-{{ :​imagealgorithms:​ltu-cl3.png?​nolink&​1200 |}} 
-\\ 
-\\ 
- 
-===== - Artificial Inteligence ===== 
- 
-LTU uses deep learning models to classify large batches of images, detect objects in an image and identify the object detected in an image. Our computer vision processes aim to provide our clients with bespoke, fast and accurate image recognition. 
- 
-LTU recognises retail products, decorated objects, pictures, book covers, textbooks, art paintings, logos, and more 
- 
-==== - Deep Learning and Transfer Learning ==== 
- 
-Based on the Transfer Learning principles, LTU implements image classification tailored for specific needs and optimizes the results returned by soliciting several image analysis algorithms. Once results are returned, this automated optimization loop tends to get as close as possible to the ground truth when the repository is poor quality or the queries are altered by exogenous factors. 
-\\ 
-\\ 
-==== - Detection and Identification ==== 
- 
- 
-Machine learning algorithms are typically designed to address specific tasks. Transfer learning is a popular method in computer vision as it allows us to build accurate models in great time efficiency by leveraging the use of pre-trained models. A pre-trained model is a model that was trained on a large benchmark dataset to solve a problem similar to the one that we want to solve. 
- 
-| {{ :​imagealgorithms:​classification_ex.png?​nolink&​450 |}} |{{ :​imagealgorithms:​classification2.png?​nolink&​700 |}}| 
- 
-LTU interface allows you to drag and drop your dataset and annotate each image using your specific terminology. 
- 
-==== -Keywords ==== 
- 
-As explained, LTU allows to associate keywords to an image that can orient the image search. Using machine learning algorithms could help to organise your dataset with keywords. 
- 
-{{ :​imagealgorithms:​keywords.png?​nolink&​600 |}} 
- 
-===== -Optical Character Recognition ===== 
-<​html>​ 
-<​table>​ 
-<td width="​30%">​ 
-Optical character recognition refers to both the technology and process of reading and converting typed, printed or handwritten characters into machine-encoded text or something that the computer can manipulate. 
-</td> 
-<td width="​20%"/>​ 
-<td width="​50%">​ 
-<img src="​http://​docs.ltutech.com/​_media/​imagealgorithms/​ocr.png?​w=400&​tok=93adf7"/>​ 
-</td> 
-</​table>​ 
-</​html>​ 
-\\ 
- 
-LTU used OCR solutions to extract text from an image. The retrieved information could help to 
-  * Improve a result given by an image search 
- 
-{{ :​imagealgorithms:​ocr-ex.png?​nolink&​600 |}} 
- 
-  * Classify images dataset 
-  * Determine keywords 
- 
-{{ :​imagealgorithms:​combinaison2.png?​nolink&​700 |}} 
- 
-====== - Image data extraction ====== 
-Thus, LTU provides solutions to combine one or several technologies presented above to extract a maximum of informations. This part illustrates what kind of information it is possible to extract and with which technologies. 
- 
-===== - Press & Fake News Monitoring ===== 
- 
-{{ :​imagealgorithms:​press.png?​nolink&​700 |}} 
- 
- 
-===== - Insurance & Personal Property ===== 
-{{ :​imagealgorithms:​stolen_art2.png?​nolink&​700 |}} 
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-===== - Education & Training ====== 
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-{{ :​imagealgorithms:​education.png?​nolink&​700 |}} 
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-===== - Retail & Consumer Apps ===== 
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-{{ :​imagealgorithms:​product.png?​nolink&​700 |}}