Abstract: Multi-layer discriminant dictionary learning has achieved remarkable results in image classification. However, the existing multi-layer discriminant dictionary learning mostly uses the alternating direction multiplier method to update the dictionary. When the image content is rich and contains multiple tags, it performs poorly in multi tag classification. The two-layer discriminant dictionary learning structure composed of recursive least square method and decorrelation enhancement reconstruction coefficient algorithm is more suitable for image multi label classification. The data is sparse decomposed many times through multi-layer discriminant dictionary learning, and the feature vectors obtained by sparse decomposition are classified by linear classifier in the last layer. The experimental results on the dress pattern data set of the Ming and Qing Dynasties verify the superiority of this algorithm. Compared with the latest existing algorithm, the classification accuracy reaches 82.17%, which achieves the best effect in similar algorithms.

Key words: RLS-DLA, ODL, Feature Sign Search, Image Classification, Supervise Learning, Dictionary Learning, Sparse Representation