Place　classification　and　object　categorization　are　necessary　functions　of　vision-based　robotic　systems.　In　this　paper,　a　novel　latent　topic　model　is　proposed　to　learn　and　recognize　scenes　and　places.　First,　each　image　in　the　training　set　is　characterized　by　a　collection　of　local　features,　known　as　codewords,　obtained　by　unsupervised　learning,　and　each　codeword　is　represented　as　part　of　a　topic.　Then,　the　codeword　distribution　of　detected　local　features　from　the　training　images　is　learned　by　performing　a　k-means　algorithm.　Next,　a　modified　Latent　Dirichlet　Allocation　model　is　employed　to　highlight　the　significant　features　(i.e.,　the　codewords　with　higher　frequency　in　the　codebook).　The　Highlighted　Latent　Dirichlet　Allocation　(HLDA)　improves　the　efficiency　of　learning　procedure.　Finally,　a　fast　variational　inference　algorithm　for　HLDA　is　proposed　to　reduce　the　computational　complexity　in　parameter　estimation.　Experimental　results　using　natural　scenes,　indoor　and　outdoor　datasets　show　that　the　proposed　HLDA　method　performs　better　than　other　counterparts　in　terms　of　accuracy　and　robustness　with　the　variation　of　illumination　conditions,　perspectives,　and　scales.　The　Fast　HLDA　is　order　of　magnitudes　faster　than　the　HLDA　without　obvious　loss　of　accuracy.　(C)　2014　Elsevier　B.V.　All　rights　reserved.