The Bag-of-Words (BOW) based methods are widely used

in image classification. However, huge number of visual information

is omitted inevitably in the quantization step of

the BOW. Recently, NBNN and its improved methods like

Local NBNN were proposed to solve this problem. Nevertheless,

these methods do not perform better than the stateof-

the-art BOW based methods. In this paper, based on the

advantages of BOW and Local NBNN, we introduce a novel

locality discriminative coding (LDC) method. We convert

each low level local feature, such as SIFT, into code vector

using the Local Feature-to-Class distance other than by

k-means quantization. Extensive experimental results on 4

challenging benchmark datasets show that our LDC method

outperforms 6 state-of-the-art image classification methods

(3 based on NBNN, 3 based on BOW).