This document describes a prototype for a figurative mark retrieval system called Figumare that allows users to search for trademark profiles by submitting a logo image. It uses a content-based image retrieval engine called LIRE to index a collection of logo images and return similar results when searching with a reference logo. The author details how LIRE works, the process used to index the logos and perform searches, and lessons learned around tuning the system for logos, which often have noise. The prototype creates a usable web interface and mobile app to interact with the LIRE engine.

1. Hands On Logo Search
Figurative Mark Retrieval Prototype
By Francisco José Alvarez Sánchez
Analyst Developer (ext) at OHIM IPR-Laboratory

2. Developing a Figurative Mark Retrieval
Prototype (Figumare)
Objective:
• Create a Proof of Concept of how a Figurative Mark Retrieval
System could work
• Document some “Lessons Learnt” about this experience.

3. The Problem
A user only has:
• a printed logo on paper or
• a logo image file or
• a URL of a logo file
and wants to know:
 the CTM Profile of the logo

4. Solution
A “Figurative Mark Retrieval Tool”
Retrieving:
• CTM-profiles with scores
When 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 CBIR
The 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 - 1
Definition of a "descriptor"
• A sequence of numbers that extracts as much
information as possible on each one of the
following image features:
o Color Histogram
o Color Layout
o Texture (Edge Histogram)
o Etc

8. LIRE Descriptors in LUCENE

9. How LIRE works - 2
The Standard algorithm
• Sequential
o Compares a given descriptor with each descriptor in the
collection
• Robust and well tested
The Space Metric Algorithm
• Not sequential
o Much faster
o It describes an image as an ordered list of similar images
o It's new, not completely tested
o 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 scores
The drawback of this method is that the descriptor of the image has to
be compared sequentially for the whole collection of images

12. The Metric Spaces Algorithm
The general idea of this method is to describe every
image by an ordered list of most similar images from
a “vocabulary of images"

13. The Metric Spaces - Example
From 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 new
descriptors

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 the
LIRE 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 URL's
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 a
picture 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 would
o Reduce the noise and
o Sharpen the edges of the figure
• Not incorporated in the prototype

18. Tuning Figumare - 2 - Collection

19. Tuning Figumare -3
The figumare application has been tested with some
degradation of logos as well as direct pictures taken
from a mobile phone camera, also it has been
compared with some logo URL’s from the internet.
Since most of the logos of the collection are black
and white, the only necessary feature is “texture”

20. Tuning Figumare - 4
Some 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 successful
project
- Clear noise from the logo collection and the logo reference
- Correct weighting of features
- New logo features OCR text descriptor