Artificial Neural Network based Relevance Feedback for Intermediate Feature Based Image Retrieval

摘要

In this paper, a new method for intermediate features based image retrieval is proposed. Image database is constructed with low level texture features obtained from Gray Level Co-Occurrence Matrix (GLCM). Semantic level queries from the user mapped to the low level features at retrieval time to retrieve the required images. Artificial Neural Network (ANN) is used in the next steps after receiving user feedbacks. Though semantics are used as search key in the initial steps, low level features are used in the ANN based searching in later steps. Back propagation algorithm is used in learning step. This ANN based relevance feedback method improves accuracy of intermediate feature based image retrieval method. Distance based method can also be used instead of ANN based method in relevance feedback stage.