In image search re-ranking, a major problem restricting the image retrieval development is a 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, Weight based Multi-Feature Fusion (WMFF) is fused by Improved Artificial Bee Colony (IABC) for presenting a re-ranking algorithm to retrieve web image. 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 Semi-supervised Consensus Clustering re-ranking with click-based similarity and typicality procedure termed as SCCCST is used in re-ranking technique. Convolutional Neural Network (CNN) classifier with Multiple Kernel Learning (CNN-MKL) is used here for performing click-based similarity. 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.