Content-based image retrieval using both positive and negative feedback

Satisfactory content-based search has long been considered a difficult task. One critical step in the content-based search is to estimate the user intention (perception) based on the query images. Our proposal is developed based on the combined weighted low-level image features. One distinct concept of our algorithm is that a sparse (scattered) feature is considered to be less important (which is not necessarily perceptually dissimilar). The other concept is that we define the image feature stability and include it in calculating the similarity measure. Yet, the third concept is using negative feedback as a pruning criterion to improve searching accuracy. Finally, quantitative simulation results are used to show the effectiveness of these concepts.