Method and apparatus for content-based image retrieval with learning function

A method of content-based image retrieval, the method comprising:

• (a) generating a plurality of training images comprising a first part and a second part;
• (b) extracting a first set of feature instances from the first part;
• (c) labeling the feature instances of the first set as positive feature instances or negative feature instances;
• (d) labeling the training images of the first part having at least one positive feature instance therein as a positive bag and labeling the training images of the first part having no positive feature instance therein as a negative bag;
• (e) generating a preliminary target concept from an amount of similarity between (i) the training images labeled as positive bags and (ii) the training images identified by a query of the second part using the positive feature instances, the preliminary target concept comprising a first subset of the first set of feature instances;
• (f) generating a revised target concept from an amount of dissimilarity between (iii) the training images labeled as negative bags and (iv) the training images identified by a query of the second part using the first subset of feature instances, the revised target concept comprising a second subset of the first set of feature instances;
• and
• (g) retrieving a set of target images in accordance with the revised target concept.

An apparatus for content-based image retrieval, comprising:

• (a) a query unit that generates a user's query;
• (b) a training instance generator that generates a set of explicit training images, retrieved from a source database, in accordance with the user's query;
• (c) a first labeling means for providing user feedback information and labeling feature instances of the explicit training images as positive feature instances or negative feature instances based on the feedback information;
• (d) a second labeling means for labeling each of the explicit training images having at least one positive feature instance therein as a positive bag and for labeling each of the explicit training images having no positive feature instance therein as a negative bag;
• (d) a training set generator that generates an implicit training image database based on the user's query;
• (e) a learned concept unit that generates a preliminary target concept from the explicit training images labeled as positive bags, generates a revised target concept in accordance with:
• (i) the explicit training images labeled as negative bags, (ii) the user feedback information, and (iii) the implicit training images, and retrieves a set of target images from the source database in accordance with the revised target concept.