Trademark Image Similarity Search View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-06-09

AUTHORS

Girish Showkatramani , Sashi Nareddi , Chris Doninger , Greg Gabel , Arthi Krishna

ABSTRACT

A trademark may be a word, phrase, symbol, sound, color, scent or design, or a combination of these, that identifies and distinguishes the products or services of a particular source from those of others. One of the crucial steps both prior to filing of the trademark applications as well as during the review of these applications is conducting a thorough trademark search to determine whether the proposed mark is likely to cause confusion with prior registered trademarks and pending trademark applications. Currently, the trademark applicants or their representatives and examining attorneys manually search the United States Patent and Trademark Office (USPTO) database that contains all of the active and inactive trademark registrations and applications. This search process relies on words and Trademark Design codes (which are hand annotated labels of design features) to search for images, thereby limiting the overall search process to primarily text-based search. For marks having image characteristics, users visually look at the image and other design characteristics and compare it with existing registered or pending trademarks to determine its uniqueness. Overall, the process of exhaustively looking at all the images that are categorized using a specific design code, while comprehensive, may take a substantial amount of time. Recently, Convolutional Networks (CNNs) have revolutionized the field of computer vision and demonstrated excellent performance in image classification and feature extraction. In this study, we utilize CNN to address the problem of searching trademarks similar to a chosen mark based on the image characteristics. A corpus of trademark images are pre-processed and then passed through a trained neural network to extract the image features. We then use these features to perform image search using the approximate nearest neighbor (ANN) variant of the nearest neighbor search (NNS) algorithm as depicted in Fig. 2. NNS is a form of proximity search that aims to find closest (or most similar) data points/items from a collection of data points/items. This system thereby seeks to provide an efficient image-based search alternate to the current keyword and category of design code combination of searching. More... »

PAGES

199-205

Book

TITLE

HCI International 2018 – Posters' Extended Abstracts

ISBN

978-3-319-92269-0
978-3-319-92270-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-92270-6_27

DOI

http://dx.doi.org/10.1007/978-3-319-92270-6_27

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104475504


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