Sparse　coding　theory　was　an　effective　method　for　finding　a　compact　representation　of　multidimensional　data.　In　this　paper,　its　application　in　the　field　of　texture　images　analysis　by　means　of　Independent　Component　Analysis　(ICA)　is　discussed.　First,　a　bank　of　basis　vectors　was　trained　from　a　set　of　training　images　according　to　it.　And　the　optimal　texture　features　were　selected　from　original　ones　which　are　extracted　by　convolving　the　test　image　with　those　basis　vectors.　Then　the　probabilities　of　these　selected　features　were　modeled　by　Gaussian　Mixture　Model　(GMM).　And　final　segmentation　result　was　obtained　after　applying　Expectation　Maximization　(EM)　algorithm　for　clustering.　Finally,　a　short　discussion　of　the　effects　of　different　parameters　(window　size,　feature　dimensions,　etc.)　was　given.　Furthermore,　combing　the　optimal　texture　features　collected　by　ICA　with　the　color　features　of　the　natural　images,　the　proposed　method　was　used　in　color　image　segmentation.　The　experimental　results　demonstrate　that　the　proposed　segmentation　method　based　on　sparse　coding　theory　can　archive　promising　performance.　©　2012　American　Scientific　Publishers　All　rights　reserved.