Re-Ranking Technique using HCC based Similarity and Typicality Process

Abstract

In image search re-ranking, a major problem restricting the image retrieval development is an intent gap,
which is a gap between user’s real intent and query/demand representation, besides well-known semantic
gap. In the past, for achieving effective web image retrieval, classifier space or feature space is explored
at a time by researchers. Visual information and images initial ranks with single feature are only
considered in conventional re-ranking techniques for measuring typicality and similarity in web image
retrieval, while overlooking click-through data influence. For image retrieval, various image features
aggregation shows its effectiveness in recent days. But, uplifting the best features impact for a specific
query image presents a major challenge in computer vision problem. In this paper, based on web query,
features are assigned with weights, where different weights are received by different queries in ranked
list. IABC algorithm used to compute weights is a data-driven algorithm and it does not require any
learning. At last, in a web, color and texture features are fused using fusion and these features are
extracted with respective modalities. A HypergraphConstruction Clustering (HCC) re-ranking with clickbased
similarity and typicality procedure termed as HCCCST is used in re-ranking technique. Its
operation is depends on selection of click-based triplet’s and a classifier is used for integrating multiple
features into a unified similarity space. The web image search re-ranking performance is greatly enhanced
using proposed technique.