Content based image retrieval with LIRe
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This document summarizes CBIR with LIRe, an open source Java-based image retrieval engine based on Lucene. It provides an overview of LIRe's features such as color histograms, MPEG-7 descriptors, and local features. It also discusses LIRe's indexing and search capabilities, examples of its use, evaluation in image retrieval benchmarks, and potential future directions.
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User Intentions or "The other end of the camera ..."
The document discusses user intentions and goals in interacting with multimedia and computer systems. It proposes that understanding user intentions can help systems better support users. It reviews research on classifying user goals in web search and digital photo retrieval. Studies found goals are difficult to classify as they can be implicit and transition between goals is fuzzy. The document also discusses using intention-based annotation tools for media production. Overall, it argues that making user intentions explicit could help bridge semantic gaps and enhance content retrieval and visualization in multimedia information management systems.
Visual Information Retrieval
This document provides an overview of visual information retrieval and content-based image retrieval. It discusses the motivation for these topics due to the large number of digital images and videos now available. Various low-level image features that can be extracted for content-based image retrieval are described, including color histograms, dominant colors, color distribution, and color correlograms. The benefits and disadvantages of different features are also outlined. The goal is to reduce images to feature vectors for efficient similarity comparison and retrieval.
Using Visual Features to Improve Tag Suggestions in Image Sharing Sites :: pr...
The document discusses using visual features to improve tag suggestions for images on sharing sites. It presents the motivation for improving tagging rates, as only 20-25% of images are currently tagged on sites like Flickr despite the obvious benefits. The authors propose an architecture to focus on the annotation process by using visual features to reduce the effort required for users to tag images. They provide an overview of the current state of their work and preliminary conclusions.
Power Laws Popularity And Interestingness
This document discusses power laws, Zipf's law, and Pareto distributions. It explains that these distributions emerge when human behavior and decisions are involved. While the theories behind these distributions are well-established, there are also practical questions around how to detect and utilize them, such as how to identify content that may become popular and optimize websites, videos, and other resources accordingly. The document concludes that these distributions are real phenomena but the exact mechanisms behind why they occur are still not fully understood.
Aspects of broad folksonomies
This document analyzes aspects of broad folksonomies based on a sample of over 800,000 bookmarks from Delicious. It finds that:
1) The frequency-rank distribution of co-occurring tags follows a power law for around 80% of tags analyzed.
2) When analyzing resource-based and user-based tagging characteristics, only around 18-13% of tags respectively followed a power law distribution, indicating folksonomies have a more complex underlying structure.
3) Tags provide added value for retrieval over title alone, though precision and recall were mostly below 0.5, showing tags are not redundant but current usage may not optimize retrieval performance.
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Content based image retrieval with LIRe
- 1. CBIR with LIRe Mathias Lux Klagenfurt University Austria This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0
- 2. Lucene Image Retrieval • Open source • Java • Based on Lucene – text retrieval engine
- 3. History • Caliph & Emir – first release in July 2004 – developed since ~ midth of 2003 • Lire as spin-off product – first release in 2006 – first use case was „barrique“ – Just MPEG-7 Barrique is a project of Roman Kern: http://sourceforge.net/projects/barrique/
- 4. Image Retrieval Features • Color Histograms • MPEG-7 features – scalable color, color layout and edge histogram • Tamura features – coarseness, contrast and directionality • Joint histograms CEDD, FCTH and JCD • Auto color correlation • JPEG coefficients histogram • Local features – SIFT , SURF, MSER • ... and more to come
- 5. Indexing and Search • Fast linear search • BoVW search based on Lucene • Parallel k-means & visual word indexing for BoVW • Fastmap for spatial indexing • Metric spaces approach for fast search in global features
- 6. Lire: Some Examples • Utilize Lucene – put an index into RAM – distribute indexes over the network • Rapid prototyping – Basic auto-tagging < 100 loc • Test different features & approaches – eg. mediamid & CEDD
- 7. Where has LIRe been used? • ImageCLEF – different groups – mentioned on the website • TRECVid – 2010 ranked 3rd in known item search • Medical domain – image retrieval & video summarization • Apparel search applications
- 8. Future Directions … • Unified way to change & test metrics • Easier benchmarking • Further support and maintenance
- 9. Thanks … • Want to download & try LIRe: • http://www.semanticmetadata.net