Abstract: The present invention provides a method for retrieving images by content measure metadata encoding. The method includes measuring selected features of a first object to form a first measurement information, encoding the first measurement information in metadata elements of a first hypertext markup language (HTML) document comprising a link to the first object, measuring selected features of a second object to form a second measurement information, encoding the second measurement information in metadata elements of a second hypertext markup language (HTML) document comprising a link to the second object, and retrieving the second object in response to the difference between the first measurement information of the first HTML document and the second measurement information of the second HTML document being less than or equal to a threshold difference value.

Abstract: A method is described that includes arranging columns of a matrix in sum total order, wherein the matrix has one or more rows, and multiple intersecting columns. Rows represent objects, and columns represent selected features. Values reside at row-column intersections and represents the frequency of the selected feature in the object The matrix is converted to a binary matrix. The columns are divided into multiple segments of equal length. Columns encompassed by each segment are replaced by a single column. Values at intersections of the rows and the single columns are set equal to numerical values indicative of ratios of a total number of the binary values of ‘1’ in a portion of the corresponding row encompassed by a segment to a total number of the binary values of ‘1’ in the corresponding row.

Abstract: A method is described that may be used to generate numerical values indicative of frequencies of selected features in objects. The method includes arranging columns of a matrix in sum total order, wherein the matrix has one or more rows, and multiple intersecting columns. Rows represent objects, and columns represent selected features. Values reside at row-column intersections. A given value at an intersection of a given row and a given column represents the frequency of the selected feature represented by the given column in the object represented by the given row. The matrix is converted to a binary matrix (e.g., comprising binary values ‘0’ and ‘1’). The columns are divided into multiple segments of equal length. Columns encompassed by each segment are replaced by a single column. Values at intersections of the rows and the single columns are set equal to numerical values indicative of ratios of a total number of one of the binary values (e.g.

Abstract: Human beings judge patterns (such as images) by complex mental processes, some of which may not be known, while computing machines extract features. By representing the human judgements with simple measurements and reducing them and the machine extracted features to a common metric space and fitting them by regression, the judgements of human experts rendered on a sample of patterns may be imposed on a pattern population to provide automatic classification.