Thesis Progress
Data
Downloaded the TU Darmstadt and TU Graz-02 databases (both available from the PASCAL object recognition database collection) and organized the images into training, validation, and test images. I am using Graz-02 for negative validation images, and Darmstadt for the other images. I ran the code against the databases to ensure things are still working properly.
Performance is quite poor on my home computer (AMD Duron 800 MHz with 384 MB memory). Matlab is many times faster on my Mac (1.25 GHz G4 and 1 GB memory). I haven’t yet decided whether to buy the toolkits necessary to finish implementation on the Mac.
Instance model design
I have focued mainly on designing the instance model, which is the majority of the work remaining for the thesis. Have considered several alternatives in part selection, representation, detection algorithm, and segmentation inputs. The instance model has two goals: to add appearance information to the shape information of the instance model, and to provide data for the region term of the graph-cut segmentation algorithm.
There are two main alternatives to part selection: pairing instance model parts with base model parts (boundary fragments), or selecting parts independently (such as with the Harris corner detector). The two main alternatives I am considering for representation are color histograms and color image patches (which would produce an instance model similar to Lebo and Leibe’s implicit shape models). Implementing model building and detection should not be difficult for any combination of these alternatives, and most of the components are already implemented in other areas of the system.
Generating data for the region term of graph cut segmentation is proving to be the biggest challenge. The difficulty is in finding a way to generate meaningful confidence values for all pixels of the image. It may be necessary to use a hybrid of instance model representation or classification methods, because a part-based model does not directly provide pixel-level confidence values at every point in the image. Some authors (such as Leibe) mention the use of uniform sampling over the test image before segmentation, so I will do some reading.
Color correlation and color histograms
Color correlation and color histogram generation are two interesting algorithms I completed this week. Correlation is similar to normalized grayscale correlation, but uses the hue and saturation components of an HSV image. Because hue is a circular value (hue is represented as the angle component of a vector in the HSV color cone), conventional arithmetic, especially mean calculation, can produce inappropriate results. I will discuss the details of the algorithm later.
I also use hue and saturation for color histograms. The color histogram is basically a histogram with an extra independent axis. A histogram bin is identified as (h, s), where h and s are hue and saturation values.