Advanced techniques for multimedia search: leveraging cues from content and structure
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Summary
This thesis develops and test a framework for automatically discovering query-adaptive multimodal search methods and proposes a new machine learning-based model for adapting the usage of each of the available search cues depending upon the type of query provided by the user.
- Type
- article
- Published
- 2009-01-01
- Cited by
- 3
- References
- 78
- OpenAlex
- https://openalex.org/W94410696
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11957408
Keywords
Computer science, Information retrieval, Leverage (statistics), Web search query, Set (abstract data type)
References
- Metasearch and Federation using Query Difficulty Prediction
- Brief Descriptions of Visual Features for Baseline TRECVID Concept Detectors
- Information retrieval, computational and theoretical aspects
- Probabilistic models for combining diverse knowledge sources in multimedia retrieval
- Translingual Information Retrieval: A Comparative Evaluation
- Solving Multiclass Learning Problems via Error-Correcting Output Codes
- The PageRank Citation Ranking : Bringing Order to the Web
- Detecting Digital Forgeries Using Bispectral Analysis
- Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval
- Using Relevance Feedback in Content-Based Image Metasearch
- Detecting image near-duplicate by stochastic attributed relational graph matching with learning
- Reranking Methods for Visual Search
- Video search reranking via information bottleneck principle
- Cross-domain video concept detection using adaptive svms
- Query-Adaptive Fusion for Multimodal Search
- Finding near-duplicate images on the web using fingerprints
- An open graph visualization system and its applications to software engineering
- Visually Searching the Web for Content
- The LIMSI Broadcast News transcription system
- How flickr helps us make sense of the world: context and content in community-contributed media collections
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