Scene retrieval by unsupervised salient part discovery
| Autor: | Sugegaya Naotoshi, Yanagihara Kentaro, Tanaka Kanji |
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| Rok vydání: | 2015 |
| Předmět: |
Vocabulary
Information retrieval Parsing Computer science business.industry media_common.quotation_subject InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Rank (computer programming) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image segmentation computer.software_genre Ranking (information retrieval) Visualization Set (abstract data type) Salient Artificial intelligence business computer media_common |
| Zdroj: | MVA |
| DOI: | 10.1109/mva.2015.7153139 |
| Popis: | While bag-of-words (BoW) scene descriptor has been widely used for scene retrieval applications, the BoW descriptor alone often fails to capture local details of a scene and produces poor results. In this paper, we address this issue by a simple effective approach, “un-supervised salient part discovery”, in which a set of salient parts are discovered via scene parsing and used as additional queries for the scene retrieval. Further, we also address the issue of discovering salient parts in a scene, and present a solution that provides similar parts for similar scenes. Multiple ranking results from the individual part queries are then integrated into a final ranking result by adopting an unsupervised rank fusion technique. Experimental results using challenging scene dataset validate the effectiveness of our app roach. |
| Databáze: | OpenAIRE |
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