diff --git a/examples/image_retrieval/binarize_features.cpp b/examples/image_retrieval/binarize_features.cpp
new file mode 100644
index 00000000000..7a7883a7e41
--- /dev/null
+++ b/examples/image_retrieval/binarize_features.cpp
@@ -0,0 +1,162 @@
+// Copyright 2014 kloudkl@github
+
+#include <cuda_runtime.h>
+#include <google/protobuf/text_format.h>
+#include <cmath>  // for std::signbit
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
+
+using namespace caffe;  // NOLINT(build/namespaces)
+
+template<typename Dtype>
+void binarize(const vector<shared_ptr<Blob<Dtype> > >& feature_blob_vector,
+              shared_ptr<Blob<Dtype> > binary_codes);
+
+template<typename Dtype>
+int features_binarization_pipeline(int argc, char** argv);
+
+int main(int argc, char** argv) {
+  return features_binarization_pipeline<float>(argc, argv);
+//  return features_binarization_pipeline<double>(argc, argv);
+}
+
+template<typename Dtype>
+int features_binarization_pipeline(int argc, char** argv) {
+  const int num_required_args = 5;
+  if (argc < num_required_args) {
+    LOG(ERROR)<<
+    "This program compresses real valued features into compact binary codes.\n"
+    "Usage: demo_binarize_features  real_valued_feature_prototxt"
+    "  feature_blob_name  save_binarized_feature_binaryproto_file"
+    "  num_mini_batches  [CPU/GPU]  [DEVICE_ID=0]";
+    return 1;
+  }
+  int arg_pos;
+
+  arg_pos = num_required_args;
+  // Check the first optional arg
+  if (argc > num_required_args && strcmp(argv[arg_pos], "GPU") == 0) {
+    LOG(ERROR)<< "Using GPU";
+    uint device_id = 0;
+    if (argc > arg_pos + 1) {
+      device_id = atoi(argv[arg_pos + 1]);
+    }
+    LOG(ERROR) << "Using Device_id=" << device_id;
+    Caffe::SetDevice(device_id);
+    Caffe::set_mode(Caffe::GPU);
+  } else {
+    LOG(ERROR) << "Using CPU";
+    Caffe::set_mode(Caffe::CPU);
+  }
+  Caffe::set_phase(Caffe::TEST);
+
+  NetParameter pretrained_net_param;
+
+  arg_pos = 0;  // the name of the executable
+
+  // Expected prototxt contains at least one data layer as the real valued
+  // features.
+  /*
+   layers {
+   layer {
+   name: "real_valued_features"
+   type: "data"
+   source: "/path/to/your/real/valued/features_leveldb"
+   batchsize: 256
+   }
+   top: "real_valued_features"
+   top: "label"
+   }
+   */
+  string real_valued_feature_prototxt(argv[++arg_pos]);
+  NetParameter real_valued_feature_net_param;
+  ReadProtoFromTextFile(real_valued_feature_prototxt,
+                        &real_valued_feature_net_param);
+  shared_ptr<Net<Dtype> > real_valued_feature_net(
+      new Net<Dtype>(real_valued_feature_net_param));
+
+  string feature_blob_name(argv[++arg_pos]);
+  CHECK(real_valued_feature_net->has_blob(feature_blob_name))
+      << "Unknown feature blob name " << feature_blob_name
+      << " in the network " << real_valued_feature_prototxt;
+
+  string save_binarized_feature_binaryproto_file(argv[++arg_pos]);
+
+  int num_mini_batches = atoi(argv[++arg_pos]);
+
+  LOG(ERROR)<< "Binarizing features";
+  vector<Blob<Dtype>*> input_vec;
+  vector<shared_ptr<Blob<Dtype> > > feature_blob_vector;
+  for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
+    real_valued_feature_net->Forward(input_vec);
+    const shared_ptr<Blob<Dtype> > feature_blob = real_valued_feature_net
+        ->blob_by_name(feature_blob_name);
+    feature_blob_vector.push_back(feature_blob);
+  }
+  shared_ptr<Blob<Dtype> > feature_binary_codes(new Blob<Dtype>());
+  binarize<Dtype>(feature_blob_vector, feature_binary_codes);
+
+  BlobProto blob_proto;
+  feature_binary_codes->ToProto(&blob_proto);
+  WriteProtoToBinaryFile(blob_proto, save_binarized_feature_binaryproto_file);
+  LOG(ERROR) << "Successfully binarized " << feature_binary_codes->num()
+      << " features!";
+  return 0;
+}
+
+// http://scikit-learn.org/stable/modules/preprocessing.html
+//   #feature-binarization
+template<typename Dtype>
+void binarize(const vector<shared_ptr<Blob<Dtype> > >& feature_blob_vector,
+              shared_ptr<Blob<Dtype> > binary_codes) {
+  CHECK_GT(feature_blob_vector.size(), 0);
+  Dtype sum;
+  size_t count = 0;
+  size_t num_features = 0;
+  for (int i = 0; i < feature_blob_vector.size(); ++i) {
+    num_features += feature_blob_vector[i]->num();
+    const Dtype* data = feature_blob_vector[i]->cpu_data();
+    for (int j = 0; j < feature_blob_vector[i]->count(); ++j) {
+      sum += data[j];
+      ++count;
+    }
+  }
+  Dtype mean = sum / count;
+  int dim = feature_blob_vector[0]->count() / feature_blob_vector[0]->num();
+  int size_of_code = sizeof(Dtype) * 8;
+  binary_codes->Reshape(num_features, (dim + size_of_code - 1) / size_of_code,
+                        1, 1);
+  uint64_t code;
+  count = 0;
+  for (int i = 0; i < feature_blob_vector.size(); ++i) {
+    for (int j = 0; j < feature_blob_vector[i]->num(); ++j) {
+      const Dtype* data = feature_blob_vector[i]->cpu_data()
+          + feature_blob_vector[i]->offset(j);
+      Dtype* binary_data = binary_codes->mutable_cpu_data()
+          + binary_codes->offset(count++);
+      code = 0;
+      int k;
+      for (k = 0; k < dim;) {
+        code |= std::signbit(mean - data[k]);
+        ++k;
+        if (k % size_of_code == 0) {
+          binary_data[(k + size_of_code - 1) / size_of_code] = code;
+          code = 0;
+        } else {
+          code <<= 1;
+        }
+      }  // for k
+      if (k % size_of_code != 0) {
+        code <<= (size_of_code - 1 - k % size_of_code);
+        binary_data[(k + size_of_code - 1) / size_of_code] = code;
+      }
+    }  // for j
+  }  // for i
+}