1. A method for retrieving and ranking multi-attribute query results according to relevance to attributes of a multi-attribute query, the method comprising: training each of a plurality of image attribute detectors for one each of a plurality of different attributes that are annotated in a training dataset of images of people; learning via a processor a plurality of pair-wise correlations between each pair of the plurality of annotated attributes from the training dataset of images; searching via the trained attribute detectors an input image dataset for images comprising at least one of a multi-attribute query subset plurality of the annotated attributes; retrieving a plurality of images from the searching of the input image dataset that each comprise at least one of the query subset plurality of attributes; and ranking the retrieved plurality of images as a function of comprising different ones of the attributes that are not within the query subset plurality of attributes but are paired to the at least one of the query subset plurality of attributes by the pair-wise correlations, wherein the ranking is an order of likelihood that the different ones of the attributes will appear in an image with the paired at least one of the query subset plurality of attributes.
1. A method for retrieving and ranking multi-attribute query results according to relevance to attributes of a multi-attribute query, the method comprising: training each of a plurality of image attribute detectors for one each of a plurality of different attributes that are annotated in a training dataset of images of people; learning via a processor a plurality of pair-wise correlations between each pair of the plurality of annotated attributes from the training dataset of images; searching via the trained attribute detectors an input image dataset for images comprising at least one of a multi-attribute query subset plurality of the annotated attributes; retrieving a plurality of images from the searching of the input image dataset that each comprise at least one of the query subset plurality of attributes; and ranking the retrieved plurality of images as a function of comprising different ones of the attributes that are not within the query subset plurality of attributes but are paired to the at least one of the query subset plurality of attributes by the pair-wise correlations, wherein the ranking is an order of likelihood that the different ones of the attributes will appear in an image with the paired at least one of the query subset plurality of attributes. 2. The method of claim 1 , further comprising: integrating computer-readable program code into a computer system comprising the processor, a computer readable memory in circuit communication with the processor, and a computer readable storage medium in circuit communication with the processor; and wherein the processor executes program code instructions stored on the computer-readable storage medium via the computer readable memory and thereby performs the steps of training each of the plurality of image attribute detectors, learning the plurality of pair-wise correlations between each pair of the plurality of annotated attributes from the training dataset of images, searching via the trained attribute detectors the input image dataset for images comprising at least one of a multi-attribute query subset plurality of the annotated attributes, retrieving the plurality of images from the searching of the input image dataset that each comprise at least one of the query subset plurality of attributes, and ranking the retrieved plurality of images as a function of comprising different ones of the attributes that are not within the query subset plurality of attributes but are paired to the at least one of the query subset plurality of attributes by the pair-wise correlations.