Towards content-based patent image retrieval: A framework perspective

Vrochidis, Stefanos; Papadopoulos, Symeon; Moumtzidou, Anastasia; Sidiropoulos, Panagiotis; Pianta, Emanuelle; Kompatsiaris, Ioannis

doi:10.1016/j.wpi.2009.05.010

Cited by 53 publications

(26 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Within this context, several systems have been developed, including PATSEEK (Tiwari and Bansal, 2004) and PatMedia image search engine (Vrochidis et al, 2010).…”

Section: Patent Image Search and Classificationmentioning

confidence: 99%

Proceedings of the Third Workshop on Vision and Language

2014

View full text Add to dashboard Cite

PrefaceThe Workshop on Vision and Language 2014 (VL'14) took place in Dublin on 23rd July 2014, as part of COLING'14. It was the joint 3rd meeting of the EPSRC Network On Vision and Language and 1st technical meeting of the new European Network on Integrating Vision and Language which is funded as a European COST Action. The VL workshops have the general aims:1. to provide a forum for reporting and discussing planned, ongoing and completed research that involves both language and vision; and 2. to enable NLP and computer vision researchers to meet, exchange ideas, expertise and technology, and form new research partnerships.As funding for the V&L EPSRC Network (EP/H018557) ends and funding for the iV&L Net European COST Action (IC1307) starts, the focus of the VL workshops will shift onto integration and joint modelling of language and vision. iV&L Net will take over the organisation of annual VL workshops for the next four years as the flagship workshop of this new COST Action.The call for papers for VL'14 was issued in May 2014 and elicited a good number of highquality submissions, each of which was peer-reviewed by three members of the programme committee. The interest in the workshop from leading NLP and computer vision researchers and the quality of submissions was high, so we aimed to be as inclusive as possible within the practical constraints of the workshop. In the end we accepted 14 submissions as long papers, and eight as short papers.The resulting workshop programme packed a lot of exciting content into one day. We were delighted to be able to include in the programme a keynote presentation by Alex Jaimes of Yahoo! Inc., an internationally leading vision researcher. Our technical programme combined seven oral papers, seven long poster papers and seven short poster papers. Some thematic clusters emerged: combined text and image processing (Nguyen et al., Sakaki et al., Jones et al., Zhang et al., HaCohen-Kerner et al.), image description, annotation and labelling (Elliott, Liparas et al., Wang et al., Jokinen and Wilcock), data set creation (Weiland et al., Le et al., McGuinness et al.), situated dialogue (Summers-Stay et al., Schütte et al.), video analysis (Bhat and Olszewska, Shrestha et al.), aids for visually impaired people (Safi et al., Belz and Bharath), and visual analysis supported by text/speech features (Anbarjafari and Aabloo). The programme also included a discussion session on future directions for the VL community and workshops, including plans for shared task competitions.We would like to thank all the people who have contributed to the organisation and delivery of this workshop: the authors who submitted such high quality papers; the programme committee for their prompt and effective reviewing; our keynote speaker, Alex Jaimes; the COLING 2014 organising committee, especially the workshops chairs, Jennifer Foster, Dan Gildea, and Tim Baldwin; the participants in the workshop; and future readers of these proceedings for your shared interest in this exciting new area of research. Aug...

show abstract

“…Within this context, several systems have been developed, including PATSEEK (Tiwari and Bansal, 2004) and PatMedia image search engine (Vrochidis et al, 2010).…”

Section: Patent Image Search and Classificationmentioning

confidence: 99%

Proceedings of the Third Workshop on Vision and Language

2014

View full text Add to dashboard Cite

show abstract

“…More recently, PATMEDIA 1 [26,22] has been presented. The proposed framework involves the application of document image pre-processing, image feature and textual metadata extraction to support efficient content-based image retrieval in the patent domain.…”

Section: Overview Of Research On Patent Image Retrievalmentioning

confidence: 99%

“…The patent drawing ontology created by Vrochidis et al [26], shown in Figure 3, specifies the five classes of figures that occur in patents: photo, diagram (including block, state and circuit diagrams), flowchart, technical drawing and graph. The automatic categorization of figures into pre-defined types is an initial step to enable the patent retrieval system to use the content information in the retrieval process.…”

Section: Relevant Work From Outside the Patent Domainmentioning

confidence: 99%

Patent image retrieval

Hanbury

Bhatti

Lupu

et al. 2011

Proceedings of the 4th Workshop on Patent Information Retrieval

View full text Add to dashboard Cite

Drawings are an important component of patents, and many search tasks in the intellectual property domain rely on the comparison of patent drawings. In this paper, we begin with a review of algorithms developed for the automated retrieval of similar images in the patent domain. There is however a larger body of research dedicated to analysis and retrieval of images found in technical documents that is also applicable to the images found in patents. We present an overview of this research for technical drawings, diagrams, charts, plots and chemical structures. Finally, we discuss the evaluation of image retrieval for patents, including short descriptions of the new patent image evaluation tasks in the CLEF-IP and TREC-CHEM evaluation campaigns in 2011.

show abstract

“…This has been extended to patent image retrieval as well. [12] and [13] propose two such systems. An image based search system called PATSEEK is detailed in [12], which uses similarity retrieval concepts to search patent documents using query images.…”

Section: Introductionmentioning

confidence: 99%

“…An image based search system called PATSEEK is detailed in [12], which uses similarity retrieval concepts to search patent documents using query images. [13] introduces indexing and image analysis techniques for patent image search and retrieval systems. Roxana Girju at Baylor University proposes a novel and innovative method [1] to automatically detect and extract causal relations from text.…”

Section: Introductionmentioning

confidence: 99%

Search for patents using treatment and causal relationships

Krishnan

Cárdenas

Springer

2010

Proceedings of the 3rd International Workshop on Patent Information Retrieval

View full text Add to dashboard Cite

An interesting area of research in information retrieval is that of relationship extraction. The ability to scan an article or set of articles and extract relationships such as "X treats Y" or "A happens because of B" is key to retrieving articles of interest to a large population.In this paper, we describe our method of identifying and extracting treatment and causal relationships from medical patent documents. We use a medical patent corpus to show that using relationship patterns to retrieve medical patent documents helps improving the recall of the system immensely. We also show that expanding our search to look for a broader set of relationships and including causal relationships along with treatment relationships, addresses a larger range of patent documents thereby improving the recall of the system significantly.

show abstract

Towards content-based patent image retrieval: A framework perspective

Cited by 53 publications

References 18 publications

Proceedings of the Third Workshop on Vision and Language

Proceedings of the Third Workshop on Vision and Language

Patent image retrieval

Search for patents using treatment and causal relationships

Contact Info

Product

Resources

About