Aiming　at　the　problems　of　low　efficiency　of　feature　extraction　and　insufficient　use　of　feature　correlation　in　image　retrieval　based　on　deep　hashing,　an　image　retrieval　method　Dual　Learning　Hashing(DLH)　that　combines　feature　layer　learning　and　hash　layer　learning　is　proposed.　In　view　of　the　problem　that　existing　methods　usually　extract　local　features　with　intensive　attention　mechanism　by　focusing　on　dense　local　regions,　these　local　regions　cannot　contain　different　local　information,　a　module　is　developed　to　learn　differentiated　local　features　by　locating　the　peaks　of　non-overlapping　subdomains　in　the　feature　map.　In　order　to　make　full　use　of　semantic　information　and　generate　high　quality　hash　code,　quantization　function　and　probabilistic　semantic-preserving　function　are　designed　in　the　hash　layer.　Finally,　combined　with　the　two　parts　of　learning,　the　total　loss　function　is　given.　A　large　number　of　comparative　experiments　have　been　carried　out　on　three　widely　used　datasets.　The　experimental　results　show　that　compared　with　other　advanced　deep　hash　methods,　DLH　has　achieved　better　retrieval　performance.　©　2023　Copyright　held　by　the　owner/author(s).