A novel image classification framework for improving image retrieval based on relevance feedback

Abstract EN

Content-based image retrieval (CBIR) is a promising technology to assist image finding.

CBIR retrieves images by visual features inherent in images.

Relevance feedback allows the user to reflect his preference to the system, then the system can reformulate the query according to the positive and/or negative examples responded by the user.

This paper presents two efficient frameworks for image classification through the analyzes of the visual features (such as color, shape, texture) of an example image.

The first framework is based on comparing the norm and the direction of vectors in multi-dimensional space for both the target and query image vectors, which allows classification according to their probabilities of existence.

The second framework depends on acquiring classification knowledge from a large empirical image database in a specific domain and utilizes that knowledge for image classification.

The initial classification process is used as a training phase to feed the system with a classification tree for images in the retrieval domain.

This tree is the best reduction of dimensionality that would result from all possible combinations of feature divisions.