Close Clustering Based Automated Color Image Annotation

TL;DR

This paper introduces an automated image annotation method using close clustering and probabilistic tagging to improve image search accuracy by reducing reliance on human-generated tags.

Contribution

It presents a novel approach combining close clustering with probabilistic tagging for automated image annotation, enhancing image retrieval accuracy.

Findings

Effective probabilistic tagging improves annotation quality

Automated system reduces manual tagging errors

Enhanced image search relevance

Abstract

Most image-search approaches today are based on the text based tags associated with the images which are mostly human generated and are subject to various kinds of errors. The results of a query to the image database thus can often be misleading and may not satisfy the requirements of the user. In this work we propose our approach to automate this tagging process of images, where image results generated can be fine filtered based on a probabilistic tagging mechanism. We implement a tool which helps to automate the tagging process by maintaining a training database, wherein the system is trained to identify certain set of input images, the results generated from which are used to create a probabilistic tagging mechanism. Given a certain set of segments in an image it calculates the probability of presence of particular keywords. This probability table is further used to generate the candidate tags for input images.