In　this　paper,　vocabulary　tree　based　large-scale　image　retrieval　scheme　is　proposed　that　can　achieve　higher　accuracy　and　speed.　The　novelty　of　this　paper　can　be　summarized　as　follows.　First,　because　traditional　Scale　Invariant　Feature　Transform　(SIFT)　descriptors　are　excessively　concentrated　in　some　areas　of　images,　the　extraction　process　of　SIFT　features　is　optimized　to　reduce　the　number.　Then,　combined　with　optimized-SIFT,　color　histogram　in　Hue,　Saturation,　Value　(HSV)　color　space　is　extracted　to　be　another　image　feature.　Moreover,　Local　Fisher　Discriminant　Analysis　(LFDA)　is　applied　to　reduce　the　dimension　of　SIFT　and　color　features,　which　will　help　to　shorten　feature-clustering　time.　Finally,　dimension-reduced　features　are　used　to　generate　vocabulary　trees　which　will　be　used　for　large-scale　image　retrieval.　The　experimental　results　on　several　image　datasets　show　that,　the　proposed　method　can　achieve　satisfying　retrieval　precision.