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Optimizing Web Search Results for Image. K-means Clustering Algorithm

  • Kartonierter Einband
  • 60 Seiten
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Academic Paper from the year 2020 in the subject Computer Science - Technical Computer Science, grade: 9.5, , language: English, a... Weiterlesen
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Beschreibung

Academic Paper from the year 2020 in the subject Computer Science - Technical Computer Science, grade: 9.5, , language: English, abstract: This paper deals with a way to optimize the search results for image searches by proposing a K-means clustering algorithm. The proposed framework attempts to optimize image search results by adopting a vectorization method which involves textual features extraction and then applying a K-means clustering algorithm to group similar images into a cluster. Hence, the aim is to develop a method that can handle a query term in a reasonably short time and return the results with higher accuracy. With each passing day, the amount of visual information on the internet, such as videos and images, is growing rapidly at an alarming rate, thereby making it difficult for a user to search for the necessary content. Users need to spend vast amounts of time in shifting through an extensive list of search results until they can find the required relevant information. To resolve this problem and to provide better image retrieval results to a user, a clustering framework is suggested in this paper. Cluster Analysis or Clustering is a concept which defines the discipline of grouping similar objects or data items into clusters. A cluster is said to be a collection of data objects. These formed clusters of similar data items differ in characteristics and features. Hence, Clustering can be defined as a solution for classifying web search results effectively for searching data items. Clustering allows users to identify their required group at a glance by looking at the cluster labels. Hence, it saves time while searching on the internet.

Produktinformationen

Titel: Optimizing Web Search Results for Image. K-means Clustering Algorithm
Autor:
EAN: 9783346348593
ISBN: 978-3-346-34859-3
Format: Kartonierter Einband
Herausgeber: GRIN Verlag
Genre: Informatik
Anzahl Seiten: 60
Gewicht: 101g
Größe: H4mm x B210mm x T148mm
Jahr: 2021