关注 ranking optimization

this paper considers both similarity and dissimilarity relationships for ranking optimization in an automatic manner to further improve person re-identification performance.

conductive similarity：if a gallery is strongly similar to the friends of the probe, it will be much likely to be a friend of the probe.
insulative dissimilarity: if a gallery is very similar to the strangers of the probe, it is much prone to differ from the probe.

propose a ranking aggregation method combining two different person reidentification methods to enhance the conductive similarity and insulative dissimilarity.

Ranking aggregation

• score-based re-ranking
• order-based re-ranking

作者考虑基于order的re-ranking：1、对异常值鲁棒 2、基于score能转换为基于order的

• similarity ranking aggregation：improve the ranking orders of quasi-similar galleries
• dissimilarity ranking aggregation：penalizes the quasi-dissimilar galleries

Framework

1. choose two different baseline methods with more complementarities
2. similarity and dissimilarity ranking aggregation
3. combine（SRA修正原始ranking，然后用DSRA修正SRA的ranking）

Similarity Ranking Aggregation

1. 从两个原始方法的top-k中选出交集作为强相似的gallery
2. 把强相似的gallery做为probe再次查询（为了增加互补性，采用cross-view based backward requery用一种方式选出来的强相似gallery，用另一种方式去requery）
3. graph-based weighted reranking
   对于2：
   
   
   where | · | denotes the cardinality and w(p, g+) is a weighting coefficient related to the original rank in G+(p). The decay factor is defined as w0 and we set w0 = 0.8 the same as [39] in all experiments.
   然后用crossed backward requery求sim2，然后合成：
   
   然后用这个相似度 rerank强相似的gallery，其他的用原始的相似度（α * RL1 + （1-α）*RL2）

Dissimilarity Ranking Aggregation

1. 从两个原始方法的last-k中选出并集作为强不相似的gallery
2. 对于每一个强不相似的gallery求其两种方法的top-k，然后求并集
3. 
4. 用这个频率去修正之前SMA中求出的rerank