Recognizing Thousands of Legal Entities through Instance-based Visual Classification

International audienceThis paper considers the problem of recognizing legal en-tities in visual contents in a similar way to named-entity recognizers for text documents. Whereas previous works were restricted to the recognition of a few tens of logotypes, we generalize the problem to the recognition of thousands of legal persons, each being modeled by a rich corporate identity automatically built from web images. We intro-duce a new geometrically-consistent instance-based classifi-cation method that is shown to outperform state-of-the-art techniques on several challenging datasets while being much more scalable. Further experiments performed on an au-tomatic web crawl of 5,824 legal entities demonstrates the scalability of the approach

DigInPix: Visual Named-Entities Identification in Images and Videos

International audienceThis paper presents an automatic system able to identify visual named-entities appearing in images and videos, among a list of 25,000 entities, aggregated from Wikipedia lists, and more specific websites. DigInPix is a generic application designed to identify different kinds of entities. In this first attempt , we only focus on logo identification (more generally on legal persons). The identification process mainly relies on an efficient CBIR system, searching in an indexed image database composed of 600,000 weak-labelled images crawled from Google Images. DigInPix proposes a responsive-design html5 interface 1 for testing purposes