With the flood of information on the Web, it has become increasingly
necessary for users to utilize automated tools in order to find, extract, filter,
and evaluate the desired information and knowledge discovery. In this research, we will
present a preliminary discussion about using the dominant meaning
technique to improve Google Image Web search engine. Google search engine
analyzes the text on the page adjacent to the image, the image caption and
dozens of other factors to determine the image content. To improve the results,
we looked for building a dominant meaning classification
model. This paper investigated the influence of using this model to retrieve
more efficient images, through sequential procedures to
formulate a suitable query. In order to build this model, the specific
dataset related to an application domain was collected; K-means algorithm was used to cluster the dataset into K-clusters,
and the dominant meaning technique is used to construct a hierarchy model of
these clusters. This hierarchy model is used to reformulate a new query. We
perform some experiments on Google and validate the effectiveness of the
proposed approach. The proposed approach is improved for in precision,
recall and F1-measure by 57%, 70%, and 61% respectively.
Cite this paper
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