In　this　paper,　the　self-adaptive　superpixels　are　generated　based　on　a　neural　network　model.　Superpixels　are　clusters　of　pixels,　which　can　simplify　the　expression　of　images.　Superpixels　are　widely　used　in　the　field　of　video/image　processing.　However,　existing　algorithms　are　mainly　based　on　hand-crafted　features,　which　will　lose　the　details　of　the　images.　We　use　the　neural　network　model　to　extract　the　deep　features　of　the　pixels　instead　of　the　hand-crafted　features.　A　predicted　object　area　is　obtained　according　to　the　results　of　the　neural　network　models.　Self-adaptive　superpixels　are　generated　by　the　clustering　algorithm　based　on　the　deep　features　of　the　pixels　and　the　predicted　object　area.　Finer　superpixels　are　generated　in　the　object　area,　and　coarser　superpixels　are　generated　in　background　area.　The　generated　self-adaptive　superpixels　can　represent　the　image　in　a　concise　way　and　improve　the　segmentation　accuracy.　Experimental　results　show　that　the　proposed　algorithm　outperforms　several　state-of-the-art　methods　on　the　BSDS500　dataset.　©　2013　IEEE.