1. An image retrieval method, comprising: learning multiple object category classifiers with a processor offline and generating classifications scores of images as the semantic attributes; and performing vocabulary tree based image retrieval using local features with semantic-aware co-indexing to jointly embed two distinct cues offline for near-duplicate image retrieval; and identifying top similar or dissimilar images using multiple semantic attributes; performing semantic-aware online querying; determining: sim ⁢ ⁢ ( q , d ) ⁢ = . ⁢ 1  S q  ⁢  S d  ⁢ ∑ v i ∈ T ⁢ ⁢ ( x i ) , v j ∈ T ⁢ ⁢ ( x j ) ⁢ ⁢ w v ⁡ ( v i , v j ) , w v ⁡ ( v i , v j ) ⁢ = . ⁢ idf 2 ⁡ ( v ) ⁢ 1 ⁢ ( v i = v j ) = log 2 ⁡ ( M M v ) , where i and j are indices, x is tree node, S is a bag of local descriptions, q a query image, d a database image, T is a vocabulary tree, w is word, idf is an inverse document frequency of node v, M is a total number of database images and M v is a number of images containing at least one descriptor that quantizes to a node v.
14. The method of claim 1 , comprising code for inserting and removing images to and from the inverted indexes based on semantic attributes.