Towards More Efficient Image Web Search

Abstract

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 F 1-measure by 57%, 70%, and 61% respectively.

Dominant Meaning Method for Intelligent Topic-Based Information Agent towards More Flexible MOOCs

“…The approach described in this section employs the approach used to create dominant meaning query and is similar to the other works cited before, in particular [12] and [20], however, it is inspired by a specific application domain coming from textual course material (e.g. video transcripts) in MOOCs.…”

“…To build the dominant meaning tree, we identify the topic of each class, and use video transcripts as a set of documents for each topic in this domain [20]. Stop words are those that occur commonly but are too generalsuch as "the, is, on, or, of, how, why, etc".…”