In　order　to　train　an　end-to-end　model　for　better　performance　in　distinguishing　superclasses　and　make　the　model　more　sensitive　to　inter-class　differences,　the　authors　propose　a　Feature　Context　Network　(FCNet),　a　novel　ensemble　framework　for　image　retrieval　based　on　clustering　(EFC)　and　metric　learning.　In　the　authors＇　approach,　the　EFC　framework　consists　of　one　common　feature　extractor　and　multiple　learning　branches.　This　tree-like　structure　not　only　promotes　the　stability　of　the　training　process　but　also　keeps　the　diversity　by　using　multiple　branches.　Each　branch　is　learnt　by　an　independent　random　re-labelling　operation　that　distributes　original　classes　into　superclasses.　In　such　a　way,　the　framework　thus　can　accelerate　the　training　process.　To　make　the　EFC　framework　focus　on　the　different　attributes　across　the　classes,　the　authors　utilize　k-means　clustering　to　divide　the　training　set　into　subsets　and　train　the　model　sequentially　with　those　subsets.　Meanwhile,　to　reduce　the　impact　of　over-fitting　problems,　the　authors　design　a　branch　weight　structure　during　training.　Further,　FCNet　fusions　different　scales　of　the　neighbourhood　information　and　consequently　makes　the　feature　map　learn　richer　information　with　the　different　receptive　fields.　Simultaneously,　it　can　reduce　the　layer　of　the　network　and　the　number　of　model　parameters.　Extensive　experiments　demonstrate　that　this　approach　outperforms　the　tested　representative　methods　on　CARS-196,　CUB-200-2011　datasets.