Image retrieval based on relevance feedback

One or more storage computer readable media comprising computer executable instructions that, when executed on a computer, direct the computer to:

• generate a query vector corresponding to a feature of one image;
• identify a feature vector corresponding to the feature of another image;
• identify a number of training samples for which relevance feedback has been received;
• if the number of training samples either equals or exceeds a threshold amount, then determine a distance between the query vector and the feature vector including transforming the query vector and the feature vector to a higher-level feature space and then assigning a weight to each element of the transformed query vector and the transformed feature vector;
• and
• if the number of training samples does not exceed the threshold amount, then determine the distance between the query vector and the feature vector including assigning a weight to each element of the query vector and the feature vector.

One or more storage computer readable media comprising computer executable instructions that, when executed on a computer, direct the computer to for one of a plurality of images and each of a plurality of features:

• generating, based on a set of search criteria, a query vector for the feature,
• identifying a feature vector, corresponding to the image, for the feature, wherein identifying the feature vector includes:
• identifying a low-level feature vector corresponding to the feature;
• and
• mapping the low-level feature vector to a higher level feature space;
• determining how closely the feature vector matches the query vector;
• and
• determining how closely the image matches the set of search criteria based on how closely, for the plurality of features, the feature vectors match the query vectors, wherein generating the query vector comprises generating the query vector based at least in part on user relevance feedback regarding how relevant images previously displayed to a user were.

One or more storage computer readable media comprising computer executable instruction that, when executed on a computer, direct the computer to generate a weight to apply to distances between query vectors and feature vectors when combining the distances, the method comprising:

• receiving feedback regarding the relevance of each image of a set of images;
• wherein fi represents a summation, over the images in the set of images, of a product of a relevance of the image and a distance between the query vector and the feature vector;
• and
• generating a weight (ui) for each of a plurality (I) of distances between a query vector corresponding to one of a plurality (I) of features and a feature vector corresponding to the one of the plurality (I) of features as:
• u
• i
• ⁢
• =
• ∑
• j
• =
• 1
• I
• ⁢
• ⁢
• f
• j
• f
• i
• .

A system comprising:

• means for generating a query vector corresponding to a feature of one image;
• means for identifying a feature vector corresponding to the feature of another image;
• means for identifying a number of training samples for which relevance feedback has been received;
• means for determining, if the number of training samples either equals or exceeds a threshold amount, a distance between the query vector and the feature vector including transforming the query vector and the feature vector to a higher-level feature space and then assigning a weight to each element of the transformed query vector and the transformed feature vector;
• and
• means for determining, if the number of training samples does not exceed the threshold amount, the distance between the query vector and the feature vector including assigning a weight to each element of the query vector and the feature vector.