Similarity retrieval of trademark images

Eakins, John P.; Boardman, J; Graham, Margaret E.

doi:10.1109/93.682526

Cited by 127 publications

(50 citation statements)

References 11 publications

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“…In fact, it seems that most of image retrieval work in the area of intellectual property is dedicated to the field of trademark search [5], [6], [7], [8], [9], [10]; however, as discussed in [11], these efforts had limited success in satisfying the user requirements.…”

Section: Related Workmentioning

confidence: 99%

Towards content-based patent image retrieval: A framework perspective

Vrochidis

Papadopoulos

Moumtzidou

et al. 2010

World Patent Information

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In this article, we discuss the potential benefits, the requirements and the challenges involved in patent image retrieval and subsequently, we propose a framework that encompasses advanced image analysis and indexing techniques to address the need for content-based patent image search and retrieval. The proposed framework involves the application of document image pre-processing, image feature and textual metadata extraction in order to support effectively content-based image retrieval in the patent domain. To evaluate the capabilities of our proposal, we implemented a patent image search engine. Results based on a series of interaction modes, comparison with existing systems and a quantitative evaluation of our engine provide evidence that image processing and indexing technologies are currently mature to be integrated in real-world patent retrieval applications.

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Section: Related Workmentioning

confidence: 99%

Towards content-based patent image retrieval: A framework perspective

Vrochidis

Papadopoulos

Moumtzidou

et al. 2010

World Patent Information

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“…Significant applications in trade mark matching have been reported [1,24], as has experimental work in fabric design pattern matching [1].…”

Section: Recovering the Desired Semantic Contentmentioning

confidence: 99%

Surveying the Reality of Semantic Image Retrieval

Enser

Sandom

Lewis

2006

Visual Information and Information Systems

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Abstract. An ongoing project is described which seeks to add to our understanding about the real challenge of semantic image retrieval. Consideration is given to the plurality of types of still image, a taxonomy for which is presented as a framework within which to show examples of real 'semantic' requests and the textual metadata by which such requests might be addressed. The specificity of subject indexing and underpinning domain knowledge which is necessary in order to assist in the realization of semantic content is noted. The potential for that semantic content to be represented and recovered using CBIR techniques is discussed.

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“…Of real-world application areas involving narrow image domains, the most studied one is undoubtedly retrieval of trademark images, typically based on shape features as the lack of background enables automatic segmentation of the images, see e.g. Eakins et al (1998), Jain and Vailaya (1998), Ciocca and Schettini (2001), King and Jin (2001), Yin andYeh (2002), or Neumann et al (2002). Other narrow domains include, among many others, different kinds of medical images (Shyu et al 1999), face recognition (Pentland et al 1994), maps (Samet and Soffer 1996) and industrial applications such as paper web defect images (Iivarinen and Pakkanen 2002).…”

Section: Semantic Gapmentioning

confidence: 99%

Content-Based Image Retrieval Using Self-Organizing Maps

Laaksonen

Koskela

Oja

1999

Visual Information and Information Systems

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Digital image libraries are becoming more common and widely used as visual information is produced at a rapidly growing rate. Creating and storing digital images is nowadays easy and getting more affordable all the time as the needed technologies are maturing and becoming eligible for general use. As a result, the amount of data in visual form is increasing and there is a strong need for effective ways to manage and process it. In many settings, the existing and widely adopted methods for text-based indexing and information retrieval are inadequate for these new purposes.Content-based image retrieval addresses the problem of finding images relevant to the users' information needs from image databases, based principally on low-level visual features for which automatic extraction methods are available. Due to the inherently weak connection between the high-level semantic concepts that humans naturally associate with images and the low-level visual features that the computer is relying upon, the task of developing this kind of systems is very challenging. A popular method to improve retrieval performance is to shift from single-round queries to navigational queries where a single retrieval instance consists of multiple rounds of user-system interaction and query reformulation. This kind of operation is commonly referred to as relevance feedback and can be considered as supervised learning to adjust the subsequent retrieval process by using information gathered from the user's feedback.In this thesis, an image retrieval system named PicSOM is presented, including detailed descriptions of using multiple parallel Self-Organizing Maps (SOMs) for image indexing and a novel relevance feedback technique. The proposed relevance feedback technique is based on spreading the user responses to local SOM neighborhoods by a convolution with a kernel function. A broad set of evaluations with different image features, retrieval tasks, and parameter settings demonstrating the validity of the retrieval method is described. In particular, the results establish that relevance feedback with the proposed method is able to adapt to different retrieval tasks and scenarios.Furthermore, a method for using the relevance assessments of previous retrieval sessions or potentially available keyword annotations as sources of semantic information is presented. With performed experiments, it is confirmed that the efficiency of semantic image retrieval can be substantially increased by using these features in parallel with the standard low-level visual features.

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Similarity retrieval of trademark images

Cited by 127 publications

References 11 publications

Towards content-based patent image retrieval: A framework perspective

Towards content-based patent image retrieval: A framework perspective

Surveying the Reality of Semantic Image Retrieval

Content-Based Image Retrieval Using Self-Organizing Maps

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