Image Search Reranking

TL;DR

This paper proposes an automatic image reranking method that combines text metadata and visual features, using classification and cross-validation to improve search result relevance without manual labeling for each query.

Contribution

It introduces a novel approach that integrates text and visual data for image reranking, enabling query-independent automatic ranking with offline supervised learning.

Findings

Improved accuracy in image reranking over baseline methods

Effective use of text metadata and visual features combined

Model applicable to various image classes without retraining

Abstract

The existing methods for image search reranking suffer from the unfaithfulness of the assumptions under which the text-based images search result. The resulting images contain more irrelevant images. Hence the re ranking concept arises to re rank the retrieved images based on the text around the image and data of data of image and visual feature of image. A number of methods are differentiated for this re-ranking. The high ranked images are used as noisy data and a k means algorithm for classification is learned to rectify the ranking further. We are study the affect ability of the cross validation method to this training data. The pre eminent originality of the overall method is in collecting text/metadata of image and visual features in order to achieve an automatic ranking of the images. Supervision is initiated to learn the model weights offline, previous to reranking process. While model learning needs manual labeling of the results for a some limited queries, the resulting model is query autonomous and therefore applicable to any other query .Examples are given for a selection of other classes like vehicles, animals and other classes.