Efficient data search using map reduce framework

Dhekale, Pratibha P.; Jichkar, Nilima

doi:10.1109/startup.2016.7583980

Cited by 2 publications

(1 citation statement)

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“…Customarily, the purpose of "MapReduce approach" [51][52][53] was designed for the actual "dynamic structure" for the "velocity", "volume" and "variety" of non-volatile large-scale datasets (or Big data [1]). In case of a geo-untagged photo, MapReduce indexing filters only the useful geo-tagged photos (that were collected in term of vectors with geo-tagging) from the set of large-scale samples which are similar to some visual contents of geo-untagged photo.…”

Section: Mapreduce Indexingmentioning

confidence: 99%

Location Estimation of a Photo: A Geo-signature MapReduce Workflow

Mookdarsanit¹,

Rattanasiriwongwut²

2017

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Location estimation of a photo is the method to find the location where the photo was taken that is a new branch of image retrieval. Since a large number of photos are shared on the social multimedia. Some photos are without geo-tagging which can be estimated their location with the help of million geo-tagged photos from the social multimedia. Recent researches about the location estimation of a photo are available. However, most of them are neglectful to define the uniqueness of one place that is able to be totally distinguished from other places. In this paper, we design a workflow named G-sigMR (Geo-signature MapReduce) for the improvement of recognition performance. Our workflow generates the uniqueness of a location named Geo-signature which is summarized from the visual synonyms with the MapReduce structure for indexing to the large-scale dataset. In light of the validity for image retrieval, our G-sigMR was quantitatively evaluated using the standard benchmark specific for location estimation; to compare with other well-known approaches (IM2GPS, SC, CS, MSER, VSA and VCG) in term of average recognition rate. From the results, G-sigMR outperformed previous approaches.

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Section: Mapreduce Indexingmentioning

confidence: 99%