ABSTRACT
Exploring large medical image sets by means of traditional similarity query criteria (e.g., neighborhood) can be fruitless if retrieved images are too similar among themselves. This demonstration introduces Kundaha, an exploration tool that assists experts in retrieving and navigating on results from a diversified similarity perspective of user-posed queries. Its implementation includes a wide set of metrics, descriptors, and indexes for enhancing query execution. Users can combine such features with diversified similarity criteria for the organized exploration of result sets and also employ relevance feedback cycles for finding new query-based viewpoints.
- Ting Deng and Wenfei Fan. 2014. On the Complexity of Query Result Diversification. TODS, Vol. 39, 2, Article 15 (2014), 46 pages. Google ScholarDigital Library
- Thomas M. Deserno, Sameer Antani, and Rodney Long. 2009. Ontology of Gaps in Content-Based Image Retrieval. J. Digital Imaging, Vol. 22, 2 (April 2009), 202--215.Google ScholarCross Ref
- Marina Drosou, H. V. Jagadish, Evaggelia Pitoura, and Julia Stoyanovich. 2017. Diversity in Big Data: A Review. Big Data, Vol. 5, 2 (June 2017), 73--84.Google ScholarCross Ref
- Marina Drosou and Evaggelia Pitoura. 2015. Multiple Radii DisC Diversity: Result Diversification Based on Dissimilarity and Coverage. TODS, Vol. 40, 1, Article 4 (March 2015), 43 pages. Google ScholarDigital Library
- Sha Hu, Zhicheng Dou, Xiaojie Wang, Tetsuya Sakai, and Ji-Rong Wen. 2015. Search Result Diversification Based on Hierarchical Intents. In CIKM. ACM, 63--72. Google ScholarDigital Library
- Lucio F. D. Santos, Willian D. Oliveira, Monica Ferreira, Agma J. M. Traina, and Caetano Traina Jr. 2013. Parameter-free and Domain-independent Similarity Search with Diversity. In SSDBM. IEEE, Article 5, 12 pages. Google ScholarDigital Library
- Marcos R. Vieira, H. L. Razente, M. C. N. Barioni, M. Hadjieleftheriou, D. Srivastava, C. Traina Jr., and V. J. Tsotras. 2011. On query result diversification. In ICDE. ACM, 1163--1174. Google ScholarDigital Library
- Hamed Zamani, Javid Dadashkarimi, Azadeh Shakery, and W. Bruce Croft. 2016. Pseudo-Relevance Feedback Based on Matrix Factorization. In CIKM. ACM, New York, NY, USA, 1483--1492. Google ScholarDigital Library
- Pavel Zezula, Giuseppe Amato, Vlastislav Dohnal, and Michal Batko. 2010. Similarity Search: The Metric Space Approach (1st ed.) Springer. Google ScholarDigital Library
- Kaiping Zheng, Hongzhi Wang, Zhixin Qi, Jianzhong Li, and Hong Gao. 2017. A survey of query result diversification. Knowledge and Information Systems, Vol. 51, 1 (2017), 1--36. Google ScholarDigital Library
Recommendations
Being Similar is Not Enough: How to Bridge Usability Gap through Diversity in Medical Images
CBMS '14: Proceedings of the 2014 IEEE 27th International Symposium on Computer-Based Medical SystemsIn this paper we present a technique developed to bridge the usability gap in Content-Based Medical Image Retrieval (CBMIR) systems exploring both similarity and diversity. Usability gaps are related to how easy to use a software tool from the ...
Content-based medical image retrieval by spatial matching of visual words
AbstractContent-Based Image Retrieval (CBIR) systems have recently emerged as one of the most promising and best image retrieval paradigms. To pacify the semantic gap associated with CBIR systems, the Bag of Visual Words (BoVW) techniques are ...
Diversified recommendation on graphs: pitfalls, measures, and algorithms
WWW '13: Proceedings of the 22nd international conference on World Wide WebResult diversification has gained a lot of attention as a way to answer ambiguous queries and to tackle the redundancy problem in the results. In the last decade, diversification has been applied on or integrated into the process of PageRank- or ...
Comments