From the webpage:
The International Conference on Similarity Search and Applications (SISAP) is an annual forum for researchers and application developers in the area of similarity data management. It aims at the technological problems shared by numerous application domains, such as data mining, information retrieval, computer vision, pattern recognition, computational biology, geography, biometrics, machine learning, and many others that need similarity searching as a necessary supporting service.
The SISAP initiative (www.sisap.org) aims to become a forum to exchange real-world, challenging and innovative examples of applications, new indexing techniques, common test-beds and benchmarks, source code and up-to-date literature through its web page, serving the similarity search community. Traditionally, SISAP puts emphasis on the distance-based searching, but in general the conference concerns both the effectiveness and efficiency aspects of any similarity search problem.
Paper submission: April 2013
Notification: June 2013
Final version: July 2013
Conference: October 2, 3, and 4, 2013
The specific topics include, but are not limited to:
- Similarity queries – k-NN, range, reverse NN, top-k, etc.
- Similarity operations – joins, ranking, classification, categorization, filtering, etc.
- Evaluation techniques for similarity queries and operations
- Merging/combining multiple similarity modalities
- Cost models and analysis for similarity data processing
- Scalability issues and high-performance similarity data management
- Feature extraction for similarity-based data findability
- Test collections and benchmarks
- Performance studies, benchmarks, and comparisons
- Similarity Search over outsourced data repositories
- Similarity search cloud services
- Languages for similarity databases
- New modes of similarity for complex data understanding
- Applications of similarity-based operations
- Image, video, voice, and music (multimedia) retrieval systems
- Similarity for forensics and security
You should be able to find one or more topics that interest you.
How similar must two or more references to an entity be before they are identifying the same entity?
Or for that matter, is similarity an association between two or more references?