Click here to load reader

Hands on Logo Search

  • View
    467

  • Download
    0

Embed Size (px)

DESCRIPTION

 

Text of Hands on Logo Search

  • 1. Hands On Logo SearchFigurative Mark Retrieval Prototype By Francisco Jos Alvarez Snchez Analyst Developer (ext) at OHIM IPR-Laboratory

2. Developing a Figurative Mark RetrievalPrototype (Figumare)Objective: Create a Proof of Concept of how a Figurative Mark RetrievalSystem could work Document some Lessons Learnt about this experience. 3. The ProblemA user only has: a printed logo on paper or a logo image file or a URL of a logo fileand wants to know: the CTM Profile of the logo 4. SolutionA Figurative Mark Retrieval ToolRetrieving: CTM-profiles with scoresWhen just a reference logo is submited from : Scanner Camara Internet Local file In a reasonable time 5. How? Obtain a CBIR Engine o (Content Based Image Retrieval) o There are many, in different architectures and licences. o See Wikipedia CBIR Engine List Create an interface to interact with the engine Tuning the CBIR with the particularities of the logos o by using a sample of logos Index the whole collection of CTM logos 6. Obtaining a CBIRThe CBIR engine LIRE has been selected because: The licence is open source (GPL) Is completely written in Java (no dependencies) Uses only Lucene for storing the descriptors of the images o DB is not needed Very well structured o Prepared for adding more features. o The internal behaviour is easy to modify o The source code is clear and well documented o Includes unit tests. The project is active 7. How LIRE works - 1Definition of a "descriptor" A sequence of numbers that extracts as muchinformation as possible on each one of thefollowing image features: o Color Histogram o Color Layout o Texture (Edge Histogram) o Etc 8. LIRE Descriptors in LUCENE 9. How LIRE works - 2The Standard algorithm Sequentialo Compares a given descriptor with each descriptor in thecollection Robust and well testedThe Space Metric Algorithm Not sequentialo Much fastero It describes an image as an ordered list of similar imageso Its new, not completely testedo Need some tuning when indexing 10. The Standard Algorithm - 1 Indexing the collection of images o For each image of the collection Extracts the list of descriptors from the image Creates a Lucene document with the following attributes: An id of the logos The related descriptor for each feature 11. The Stantard Algorithm - 2 Searching an image o Extracts the list of descriptors from the reference image o For each Lucene document in the collection For each feature Calculate distance between both descriptors Calculate average distance applying weightings o Order the distances and calculate score o Returns the id of the logos with the highest scoresThe drawback of this method is that the descriptor of the image has tobe compared sequentially for the whole collection of images 12. The Metric Spaces AlgorithmThe general idea of this method is to describe everyimage by an ordered list of most similar images froma vocabulary of images" 13. The Metric Spaces - ExampleFrom a collection of 300,000 images Index the collection of images o Select a random subset of 1,000 images called s1 o Index s1 in a new lucene index l1 by using the classic method o For each image in the collection of 300,000 images Search the 50 most similar from the 1,000 images in s1 Add the ordered list of 50 identificators as newdescriptors 14. The Metric Spaces - Example Cont Searching an image o Search the 50 most similar from the 1,000 images in s1 o Query in Lucene by comparing the sequence of 50 ordered ids o The footrule distance for comparing ranked vectors is used 15. Create an interface to interact with theLIRE engine The interface is a Spring Web Application written in Grails The services of the system allows the following: o Index a collection from a directory o Index a collection from a list of URLs o Search by an uploaded logo o Search by an URL of a logo An Android application has being created so it is possible to take apicture of a logo with a mobile phone and search it directly. 16. Figumare Web Interface 17. Tuning Figumare - 1 Collection size 678 logos Vienna Code: Geometrical Figures" A great percentage of the CTMs have noise Using ImageMagick before indexing wouldo Reduce the noise ando Sharpen the edges of the figure Not incorporated in the prototype 18. Tuning Figumare - 2 - Collection 19. Tuning Figumare -3The figumare application has been tested with somedegradation of logos as well as direct pictures takenfrom a mobile phone camera, also it has beencompared with some logo URLs from the internet.Since most of the logos of the collection are blackand white, the only necessary feature is texture 20. Tuning Figumare - 4Some photos of logos to search: 21. Figumare Search Example 22. Conclusion- Adopting a CBIR tool for logo search is possible- The Tuning Phase is decisive in order to have a successfulproject- Clear noise from the logo collection and the logo reference- Correct weighting of features- New logo features OCR text descriptor

Search related