Image retrieval apparatus

An image retrieval method comprising:

• estimating a relevance of images in an image database and a user's intention to select one or more unlabelled images from the image database where the one or more unlabelled images selected are one or more images most irrelevant to the user's intention;
• and
• training in a round of a retrieval feedback, estimating relevance of images in the image database and the user's intention, and determining retrieval results by a main learner, wherein the main learner uses the selected one or more unlabelled images;
• and
• selecting, in each feedback round and according to estimation results of the main learner, one or more unlabelled images from the image database for the user to label;
• wherein the selecting includes:
• calculating information capacity of each of the one or more unlabelled images in the image database, and selecting the one or more unlabelled images to be provided to the user to label in accordance with calculation results, and
• generating image samples for a user to label, the image samples determined to be most important to a user based on the user's intention, wherein the estimating builds a classifier to select a highest number of negative samples of the one or more unlabelled images.

A method of an image retrieval apparatus which includes a feedback analysis unit, a user interface unit and an active selector, the method comprising:

• estimating a relevance of images in an image database and a user's intention to select one or more unlabelled images from the image database where the one or more unlabelled images selected are one or more images most irrelevant to the user's intention;
• training in a round of a retrieval feedback, estimating relevance of images in the image database and the user's intention, and determining retrieval results by a main learner, wherein the main learner uses the selected one or more unlabelled images;
• and
• selecting, in each feedback round and according to estimation results of the main learner, one or more unlabelled images from the image database for the user to label;
• wherein the selecting includes:
• calculating representativeness of each of the unlabelled images in the image database, and selecting one or more unlabelled images to be provided to the user to label in accordance with calculation results,
• wherein the estimating builds a classifier to select a highest number of negative samples of the one or more unlabelled images.

An image retrieval apparatus which includes a feedback analysis unit, a user interface unit and an active selector, the image retrieval apparatus comprising:

• an unlabelled image selector to estimate a relevance of images in an image database and a user's intention to select one or more unlabelled images from the image database, where the one or more unlabelled images selected are one or more images most irrelevant to the user's intention,
• a main learner to train in a round of a retrieval feedback, estimating relevance of images in the image database and the user's intention, and determining retrieval results, wherein the main learner uses the one or more unlabelled images selected by the unlabelled image selector in the estimating;
• and
• the active selector to select, in each feedback round and according to estimation results of the main learner, one or more unlabelled images from the image database for the user to label, the active selector including an information capacity measurement calculation unit and a selection unit,
• wherein the information capacity measurement calculation unit calculates the information capacity of each of the one or more unlabelled images in the image database, and the selection unit selects the one or more unlabelled images to be provided to the user to label in accordance with calculation results of the information capacity measurement calculation unit, and
• wherein the information capacity measurement calculation unit calculates the information capacity of an image by calculating a distance between the image and a current classification boundary determined by the main learner.