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+# CS4185 Multimedia Technologies and Applications
+
+## What does this program do?
+
+- Loads aninput imageand 1000 database imagesto be compared with it.
+
+- Converts the images to grayscale
+
+- Compares the base image with the database image using pixel-by-pixel difference.
+
+- Displays the numerical matching parameters obtained.
+
+- Displays the input image and the best match result.
+
+## Basic Requirements (80%)
+
+- Automatically changing some setting according to the extracted features is allowed.
+
+Students are required to finish the following four tasks in the basic requirements:
+
+1.  Improve the number of correctly matched images (20%)
+1.  Modify the above program to retrieve similar images (20%)
+
+    - Utilize color information.
+
+      - Color Models
+
+        - The RGB Color Models
+          - Quantization
+
+      - Color Histograms
+        - disadvantages
+          1. Color similarity across histogram bins is not considered
+          1. Spatial color layout is not considered
+
+    - Using different layout.
+
+      - Color Layout
+        - http://en.wikipedia.org/wiki/Color_layout_descriptor
+        - Need for Color Layout -> Global color features give too many false positives.
+        - How it works:
+          - Divide the whole image into sub-blocks.
+          - Extract features from each sub-block.
+        - Can we go one step further?
+          - Divide the image into regions based on color feature concentration.
+          - This process is called segmentation
+
+    - Utilize edge and shape information.
+
+      - Circle Hough transform:
+
+        - https://en.wikipedia.org/wiki/Circle_Hough_Transform
+        - http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.html
+
+      - segmentation
+
+        - Features for local regions in the image
+        - Interest points: corners, edges and others
+        - Keypoints:
+          - points in images, which are invariant to image translation, scale and rotation, and are minimally affected by noise and small distortions
+        - Scale-invariant feature transform (SIFT) by David Lowe
+
+          - Image content is transformed into local feature coordinates that are invariant to translation, rotation, scale, and other imaging parameters
+          - advantages:
+
+            - Locality:features are local, so robust to occlusion and clutter (no prior segmentation)
+            - Distinctiveness:individual features can be matched to a large database of objects
+            - Quantity:many features can be generated for even small objects
+            - Efficiency:close to real-time performance - Extensibility:can easily be extended to wide range of differing feature types, with each adding robustness
+
+          - Detect keypoints using the SIFT detector
+            - `tutorial3` -> page 13 ~ 18
+          - R-trees, SR-Trees ?
+
+    - Features fusion
+      - Image Features Measures
+      - Image Distance Measures
+      - db
+        - tutorial4 -> page 9
+
+1.  Improve on the Precision (20%)
+
+    - The percentage of retrieved images that are matched
+
+1.  Improve on the Recall (20%)
+
+    - the percentage of matched images that are retrieved.
+
+## The extension includes two parts,
+
+technical improvementand UI design.
+
+### The technical improvement :
+
+- 15% of marks will be given based on the technical difficulties
+- may include new retrieval algorithms
+  - (e.g., 80+% of precision and 55+% of recall),
+- high dimensional data indexing
+  - (efficiently storing and managing the features extracted from the database, modifying the program so that it does not need to compute the features every time),
+- retrieval algorithms for particular types of images(e.g., sunset images, images containing human faces),
+- a crawler to obtain images from the internet, or adding semantic informationto help improve the retrieval performance.
+
+### UI design
+
+- 10% will be given based on the UI design.
+
+### Submission
+
+- Program
+- Demo
+- Report