The current cross-scenario clothing retrieval framework is based on the torso of the body. The retrieval results are refined by clothing recognition

which leads to lost clothing information and cross-scenario clothing recognition. We proposed a new clothing segmentation algorithm and clothing recognition method

which were based on domain-adaptive dictionary learning. First

we proposed an over-segmentation hierarchical fusion algorithm with pose estimation to segment intact clothing items and retrieve similar clothing images. During clothing recognition

the intermediate domain dictionaries between product clothing dataset and daily clothing dataset were sequentially learned to improve the accuracy of classifiers and the adaptability of the dictionary for clothing style recognition in different scenarios. To verify the efficiency of the proposed method

experiments were performed with Fashionista dataset

which a large public datasets

and our developed datasets. Experiments showed that the precision of the proposed method was 62.1% and 63.4% for the upper body and lower body

respectively

which indicated that the proposed method outperformed state-of-the-art methods in terms of clothing segmentation

clothing recognition

and clothing retrieval. To address the problem of current cross-scenario clothing retrieval

we proposed a novel hierarchical fusion clothing segmentation algorithm and domain-adaptive dictionary learning to recognize clothing attributes. The proposed method ensures the integrity of clothing and improves the precision of cross-scenario retrieval and style recognition.