SearchImage

1. SearchImage OHIM Lab Vision Initial approach

2. OHIM Laboratory has been working in figurative Search for trademarks and now is trying generalise to designs in the framework of project SearchImage Figurative search is difficult but we can expect progressive improvements, mainly reduction of “noise” (false positives) Different approaches must be applied; they should integrate, evolve & be cumulated Systems should self-learn from human search We will not reach a full automatic solution we think that the best result will come from semiautomatic systems OHIM Laboratory findings

3. Codification Improvement: dominant operator, vector codification,… Semi-automatic codification: Automatic but revised by humans Promotion of a uniform, neutral background for the representation of designs Federation of databases (sharing metadata with other offices) Improvement in search algorithms New Ideas

4. The original ideas about codification improvement came from Vienna codification for trademark, but now we are trying to generalise to Designs Some codes or tags should be marked with the “dominant” operator (+) meaning that this feature is a “dominant” or a “distinctive” one in the image. For example colour tags: +red blue white Incompatible codes. A list of impossible combinations will reduce the noise in the search Codification Improvement

5. We successfully developed a prototype that tags dominant colours in an image Some manual checking is needed for some image (i.e. for unclear background or shadows) The dominant operator (+) is consequently further improved by adding ratios (vectors): i.e. +red (60%), blue (5%), white(4%). Vectorisation is not easy with human codifiers We are investigating for semiautomatic tagging shape and texture The semi-automatic codification will reduce noise but also time of search Semi-automatic codification

6. Promotion of a uniform, neutral background for the representation of designs to be used by applicants Degradation of colours in the background or shadows “confuse” automatic engines The promotion of a uniform background with no shadows and touching the 4 edges or the image frame will improve the automatic codification Neutral background

7. Some offices have codes, tags, descriptions or any other metadata that federated will improve the description of the image and consequently the quality of search Standards for interchange of metadata should be needed Methods for recognising identical images are needed in order to interchange metadata Agreements between offices are needed Federation of metadata

8. The algorithm for search must be based in distances (continuum) and dominance of the measured feature in the image in order to weight the different features (dominant operator or vector) The algorithm must consider the distinctiveness of a particular feature in the domain of search in order to weight it Statistical (references) & tuning tools to test hypothesis and improvements Self-learning mechanism (neural systems?) Improvement in search algorithms