Automatic Image Annotation using Possibilistic Clustering Algorithm

Abstract

In this paper, the proposed PCMRM (possibilistic based cross-media relevance model) annotates images based on their visual contents. PCMRM framework relies on unsupervised learning to group the visually similar image regions into homogeneous clusters, along with the cross-media relevance model (CMRM) that is used to estimate the joint distribution of textual keywords and images. Besides, the unsupervised learning task exploits the robustness to noise of a possibilistic clustering algorithm, and generates membership degrees that represent the typicality of image regions with respect to the obtained clusters. To validate and assess the proposed system, we used the standard Corel dataset. PCMRM produced promising results. The reported performance measures proved that the proposed automatic image annotation approach outperforms similar state of the art solutions. This attainment is mainly attributed to the exploitation of the possibilistic membership produced by the clustering algorithm which allowed accurate learning of the association between annotating labels and the visual content of the image regions.

Predicting the Persuasiveness of Influence Strategies From Student Online Learning Behaviour Using Machine Learning Methods

“…The support vector machine model built achieved impressive performance. Furthermore, Ismail et al (2019) applied possibilistic clustering to group visually similar image regions into clusters. The clustering process produced promising results that facilitate automatic image annotation.…”

“…For instance, a study applied k-means clustering to characterize the interaction behaviour of learners and the result was used to train a supervised classifier to predict successful and unsuccessful groups of learners (Amershi & Conati, 2009). Also, several studies employed clustering in discovering knowledge to improve the training of classifiers (Gan et al, 2013; Ismail et al, 2019). Based on previous studies that have applied the clustering technique and used the results to train classifiers, our framework employed the clusters identified to train supervised classifiers.…”

An Insight on Image Annotation Approaches and their Performances

“…Ismail, Alfaraj and Bchir [18] used PCMRM framework relied on visually similar image regions into homogeneous clusters, to evaluate the joint distribution of textual keywords and images. The results were compared with other state-of-the-art algorithms to show the superiority.…”