Collaborators: Greg Hager (JHU), Maneesh Dewan (Siemens)

We study methods that attempt to integrate information from coherent image regions to represent the image. Our novel sparse image segmentation can be used to solve robust region correspondences and therefore constrain the search for point correspondences. The philosophy behind this work is that coherent image regions provide a concise and stable basis for image representation: concise meaning that the required space for representing the image is small, and stable meaning that the representation is robust to changes in both viewpoint and photometric imaging conditions.

In addition, we have proposed a subspace labeling technique for global Image segmentation in a particular feature subspace is a fairly well understood problem. However, it is well known that operating in only a single feature subspace, e.g. color, texture, etc, seldom yields a good segmentation for real images. However, combining information from multiple subspaces in an optimal manner is a difficult problem to solve algorithmically. We propose a solution that fuses contributions from multiple feature subspaces using an energy minimization approach. For each subspace, we compute a per-pixel quality measure and perform a partitioning through the standard normalized cut algorithm. To fuse the subspaces into a final segmentation, we compute a subspace label for every pixel. The labeling is computed through the graph-cut energy minimization framework proposed by Boykov et al. Finally, we combine the initial subspace segmentation with the subspace labels obtained from the energy minimization to yield the final segmentation.

Publications:

[1]	J. J. Corso and G. D. Hager. Image Description with Features that Summarize. Computer Vision and Image Understanding, 113:446-458, 2009. [ bib | .pdf ]
[2]	J. J. Corso and G. D. Hager. Coherent Regions for Concise and Stable Image Description . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 184-190, 2005. [ bib | .pdf ]
[3]	J. J. Corso, M. Dewan, and G. D. Hager. Image Segmentation Through Energy Minimization Based Subspace Fusion. In Proceedings of 17th International Conference on Pattern Recogntion (ICPR 2004), 2004. [ bib | .pdf ]
[4]	J. J. Corso, M. Dewan, and G. D. Hager. Image Segmentation Through Energy Minimization Based Subspace Fusion. Technical Report  CIRL-TR-04-01, The Johns Hopkins University, 2004. [ bib | .pdf ]

We represent by a Gaussian kernel to facilitate continuous optimization techniques in detect (and registration). The kernels are applied to scalar projections of the image; the intuition is that various projection functions will map a region of consistent image content to a homogeneous image patch in the scalar field. Below, we show an image, its projection under neighborhood variance and the extracted regions. (The regions are drawn as ellipses corresponding to 3 standard deviations of the kernel.)

Here, we show the same image and its complete region-representation in color space.

We describe each region by the vector of kernel-weighted means under all projections that were used during detection. This representation is both concise and stable. We show a table comparing its memory footprint versus two other methods (Lowe's SIFT and Carson, Malik et al.'s Blobworld). These results are for our dataset (discussed below). We are grateful to both of the other groups for providing their source code/binaries which facilitated this analysis.

	Average Number of Elements	Size per element (in Words)	Average size (in Words)
Our Technique	159	5	797
Blobworld	9	239	2151
SIFT	695	32	22260

Next, we show empirical evidence that our approach is stable under gross viewpoint changes. We distort the input image by various affine transformations and detect the regions. From the figure below, we can see that roughly the same regions are detected. Note, that our current kernel is axis-aligned; in order to make the kernel fully affine-invariant we would have to add a 5 parameter for rotation and a 6 (a pre-kernel image warp) for image skew.

Currently, we use a simple nearest neighbor analysis on the regions' feature vector to measure similarity and voting (over the whole database) for image retrieval. Below is an example of both a positive and negative match.

Positive

Negative

To compare our approach to the other two approaches mentioned earlier, we performed an image retrieval experiment on a set of 48 images taken of the same, indoor scene. All images can be found here. Two images are considered matching if there is an pixel-area overlap. We use the standard precision (fraction of true-positive matches from all retrieved) and recall (fraction of matching images retrieved against the total possible matching images in the database). A sample of the database is below.

We see the SIFT performs the best for the standard retrieval experiment. This results agrees with those found by Mikolajczyk and Schmid (CVPR, 2003). Note that SIFT is storing substantially more information that both our method and Blobworld. Next, we compare out method to SIFT after distorting the query images drastically by halving the aspect ratio. We find that our method is very robust to such a change and surpasses SIFT in the precision-recall plot.

Subspace Fusion for Global Segmentation

We have proposed a subspace labeling technique for global Image segmentation in a particular feature subspace is a fairly well understood problem. However, it is well known that operating in only a single feature subspace, e.g. color, texture, etc, seldom yields a good segmentation for real images. However, combining information from multiple subspaces in an optimal manner is a difficult problem to solve algorithmically. We propose a solution that fuses contributions from multiple feature subspaces using an energy minimization approach. For each subspace, we compute a per-pixel quality measure and perform a partitioning through the standard normalized cut algorithm. To fuse the subspaces into a final segmentation, we compute a subspace label for every pixel. The labeling is computed through the graph-cut energy minimization framework proposed by Boykov et al. Finally, we combine the initial subspace segmentation with the subspace labels obtained from the energy minimization to yield the final segmentation. Examples of the algorithms being studied follow: