We review recent advances in image retri . The two fundamental

components of a retri system, representation and learning,

are analyzed. Each component is decomposed into its constituent

building blocks: features, feature representation, and similarity

function for the representation short- and long-term procedures

for learning. We identify a series of requirements for each of the

sub-areas, e.g. optimality, invariance, perceptual relevance, computational

tractability, and point out various approaches proposed

to satisfy them. Several open problems are also identified-We review recent advances in image retri . The two fundamental

components of a retri　system, representation and learning,

are analyzed. Each component is decomposed into its constituent

building blocks: features, feature representation, and similarity

function for the representation　short- and long-term procedures

for learning. We identify a series of requirements for each of the

sub-areas, e.g. optimality, invariance, perceptual relevance, computational

tractability, and point out various approaches proposed

to satisfy them. Several open problems are also identified