Image　saliency　detection　is　a　very　helpful　step　in　many　computer　vision-based　smart　systems　to　reduce　the　computational　complexity　by　only　focusing　on　the　salient　parts　of　the　image.　Currently,　the　image　saliency　is　detected　through　representation-based　generative　schemes,　as　these　schemes　are　helpful　for　extracting　the　concise　representations　of　the　stimuli　and　to　capture　the　high-level　semantics　in　visual　information　with　a　small　number　of　active　coefficients.　In　this　paper,　we　propose　a　novel　framework　for　salient　region　detection　that　uses　appearance-based　and　regression-based　schemes.　The　framework　segments　the　image　and　forms　reconstructive　dictionaries　from　four　sides　of　the　image.　These　side-specific　dictionaries　are　further　utilized　to　obtain　the　saliency　maps　of　the　sides.　A　unified　version　of　these　maps　is　subsequently　employed　by　a　representation-based　model　to　obtain　a　contrast-based　salient　region　map.　The　map　is　used　to　obtain　two　regression-based　maps　with　LAB　and　RGB　color　features　that　are　unified　through　the　optimization-based　method　to　achieve　the　final　saliency　map.　Furthermore,　the　side-specific　reconstructive　dictionaries　are　extracted　from　the　boundary　and　the　background　pixels,　which　are　enriched　with　geometrical　and　visual　information.　The　approach　has　been　thoroughly　evaluated　on　five　datasets　and　compared　with　the　seven　most　recent　approaches.　The　simulation　results　reveal　that　our　model　performs　favorably　in　comparison　with　the　current　saliency　detection　schemes.