＂Curse　of　Dimensionality＂　is　one　of　the　important　problems　that　Content-Based　Image　Retrieval　(CBIR)　confronts.　Dimensionality　reduction　is　an　effective　method　to　overcome　it.　In　this　paper,　six　commonly-used　dimensionality　reduction　methods　are　compared　and　analyzed　to　examine　their　respective　performance　in　image　retrieval.　The　six　methods　include　Principal　Component　Analysis　(PCA),　Fisher　Linear　Discriminant　Analysis　(FLDA),　Local　Fisher　Discriminant　Analysis　(LFDA),　Isometric　Mapping　(ISOMAP),　Locally　Linear　Embedding　(LLE),　and　Locality　Preserving　Projections　(LPP).　For　comparison,　Scale　Invariant　Feature　Transform　(SIFT)　and　color　histogram　in　Hue,　Saturation,　Value　(HSV)　color　space　are　firstly　extracted　as　image　features,　meanwhile　SIFT　feature　extraction　procedure　is　optimized　to　reduce　the　number　of　SIFT　features.　Then,　PCA,　FLDA,　LFDA,　ISOMAP,　LLE,　and　LPP　are　respectively　applied　to　reduce　the　dimensions　of　feature　vectors,　which　can　be　used　to　generate　vocabulary　trees.　Finally,　we　can　process　large-scale　image　retrieval　based　on　the　inverted　index　built　by　vocabulary　trees.　In　the　experiments,　the　performance　of　various　dimensionality　reduction　methods　are　analyzed　comprehensively　by　comparing　the　retrieval　performance,　advantages　and　disadvantages,　computational　complexity　and　time-consuming　of　image　retrieval.　Through　a　series　of　experiments,　we　can　conclude　that　dimensionality　reduction　method　of　LLE　and　LPP　can　effectively　reduce　computational　complexity　of　image　retrieval,　while　maintaining　high　retrieval　performance.　(C)　2014　Elsevier　B.V.　All　rights　reserved.