CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

The magic words: Using computers to uncover mental associations for use in magic trick design

This work was supported by EPSRC grant number EP/J50029X/1

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Semantic retrieval of trademarks based on conceptual similarity

Trademarks are signs of high reputational value. Thus, they require protection. This paper studies conceptual similarities between trademarks, which occurs when two or more trademarks evoke identical or analogous semantic content. This paper advances the state-of-the-art by proposing a computational approach based on semantics that can be used to compare trademarks for conceptual similarity. A trademark retrieval algorithm is developed that employs natural language processing techniques and an external knowledge source in the form of a lexical ontology. The search and indexing technique developed uses similarity distance, which is derived using Tversky's theory of similarity. The proposed retrieval algorithm is validated using two resources: a trademark database of 1400 disputed cases and a database of 378,943 company names. The accuracy of the algorithm is estimated using measures from two different domains: the R-precision score, which is commonly used in information retrieval and human judgment/collective human opinion, which is used in human-machine systems

Multi-faceted Assessment of Trademark Similarity

Trademarks are intellectual property assets with potentially high reputational value. Their infringement may lead to lost revenue, lower profits and damages to brand reputation. A test normally conducted to check whether a trademark is highly likely to infringe other existing, already registered, trademarks is called a likelihood of confusion test. One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1,400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting. Keywords—Trademark assessment, trademark infringement, trademark retrieval, degree of similarity, fuzzy aggregation, semantic similarity, phonetic similarity, visual similarity

Evaluation of the potential interest of Italian retail distribution chains for Kamut-based products

Kamut® is a registered trademark cereal (an organic crop) whose origins are much older; it has Egyptian origins and at the present time it is grown in different areas of the world (mainly in Montana and Canada), but considering the agronomic requirements of this crop, the tests in Thailand are the most reliable. Evaluation of the interest of retail distribution chains for Kamut-based products belongs to the researches regarding the evaluation of the quality food products trade. In this case, countries involved are Thailand, one of the most important producers, and Italy, one of the most important market of the EU. Qualitative analysis technique was the most appropriate tool. This survey consisted of in-depth semi-structured interviews directed at Italian large scale retails (purchasing and marketing managers). The final information were obtained through a subjective analysis of the content of the interviews summaries, a statistical analysis of the content of the interviews and the creation of conceptual positioning maps. The awareness of the product, the communicative factor, the consumers reactions, the specific requirements of the distribution chains and production areas are some of the most important elements that can influence the creation and the development of a international trade relationship.organic products, quality food products, international trade, Italian large scale retail, qualitative analysis technique, Agribusiness, Marketing,

CNN-Siamese 네트워크를 활용한 문자 상표 발음 유사성 탐지

학위논문(석사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 조성준.Recently, as the number of registered trademarks has rapidly increased, research to determine trademark similarity based on machine learning has been actively con- ducted. Similarity of trademarks is judged based on shapes, meaning, and pronun- ciation. In the case of pronunciation, there is a limit in judging similarity because the standards for similarity are ambiguous and spellings do not correspond to pro- nunciation in many cases. On the other hand, the performance of converting text into speech has been remarkably improved due to the recent development of speech synthesis technology. In this paper, we propose a deep learning framework that au- tomatically determines the pronunciation similarity of trademarks using speech data converted using speech synthesis technology. First, after synthesizing the trademark text into speech, it is converted into a log Mel spectrogram, and feature learning is performed through a convolutional neural network with a triplet loss. To compare the proposed method with previous studies, the trademark text dataset provided by AIhub was used, and our proposed method showed superior performance than the previous studies.최근 등록되는 상표의 수가 빠르게 증가함에 따라 기계학습을 기반으로 상표 유사성을 판단하려는 연구가 활발히 진행되어 왔다. 상표의 유사성은 도형, 관념, 발음을 기준으 로 판단되는데, 발음의 경우 유사함의 기준이 모호하며 철자가 발음에 대응되지 않는 경우가 많기 때문에 유사성을 판단하는데 한계가 존재한다. 한편, 최근 음성 합성 기술의 발달로 인해 텍스트를 음성으로 변환하는 성능이 눈에 띄게 향상하였다. 본 논문은 음 성합성기술을 활용하여 상표의 발음 유사성을 자동으로 판단하는 딥러닝 프레임워크를 제안한다. 먼저, 상표 텍스트를 음성으로 합성한 뒤, log Mel Spectrogram 으로 변환 하고 합성곱 신경망과 삼중항 손실을 통해 feature 학습을 진행한다. 제안하는 방법과 선행 연구를 비교하기 위해 AIhub 에서 제공하는 상표 텍스트 데이터셋을 활용하였고, 제안하는 방식이 선행 연구를 앞서는 것을 확인하였다.Chapter 1 Introduction 1 Chapter 2 Related Work 5 Chapter 3 Proposed Method 8 3.1 Model Architecture 8 3.2 EvaluationMetric 12 Chapter 4 Datasets 14 4.1 Traindataset 14 4.2 Testdataset 15 4.3 Speechdataset 15 4.4 Preprocessing 15 Chapter 5 Experimental Results 18 5.1 Experiment1: Compare different input type 18 5.2 Experiment 2: Compare signal processing methods 19 5.3 Experiment3:Comparebackbonenetworks 20 5.4 Experiment4:Comparebaselinemodels 21 Chapter 6 Conclusion 23 Bibliography 25 국문초록 28 감사의 글 29석

Looking at the Lanham Act: Images in Trademark and Advertising Law

Words are the prototypical regulatory subjects for trademark and advertising law, despite our increasingly audiovisual economy. This word-focused baseline means that the Lanham Act often misconceives its object, resulting in confusion and incoherence. This Article explores some of the ways courts have attempted to fit images into a word-centric model, while not fully recognizing the particular ways in which images make meaning in trademark and other forms of advertising. While problems interpreting images are likely to persist, this Article suggests some ways in which courts could pay closer attention to the special features of images as compared to words

Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms