[11] A Similarity Measure for Motion Stream Segmentation and Recognition (Li & Prabhakaran – 2005)

Current Mood: studious

Blogs I Commented On:

• Kevin Wei
• Paul Corey
• Josh Johnston

Summary:
The paper states that it’s a motion segmentation paper, which basically seems like gesture segmentation for a wider area. Given that directional and angular values in a motion is recorded in a matrix format for a motion, where columns measure the attributes and rows record the times of that motion, similarity between these motions can be compared given the same number of columns (i.e., attributes) yet different number of rows (i.e., times). This similarity comparison can be done using singular value decomposition (SVD), which reveals the geometric structure of a matrix. If two motions are similar, their corresponding eigenvectors should be parallel to each other, and their corresponding eigenvalues should be proportional to each other, thus only eigenvectors and eigenvalues from the matrices of the two motions are considered. Their similarity measure equation, which is dependent on the eigenvectors and eigenvalues, rely on some integer k, for 1 < k < n, where k determines the number of eigenvectors given n attributes in a motion matrix. For this paper, k = 6 from empirical testing. Given the significance of k, this non-metric similarity measure is called the k Weighted Angular Similarity (kWAS), which captures the angular similarities of the first k corresponding eigenvector pairs weighted by the corresponding eigenvalues. To recognize motion streams, the paper assumes a minimum length l and a maximum length L. Their kWAS method is done incrementally to segment the streams for motion recognition taken from a Cyberglove and cameras.

Discussion:
The paper claims accuracy rates in the high 90s, but as Brandon pointed out in class, those rates stemmed from artificially-produced samples. Since that was the case, there really weren’t any results for their motion segmentation algorithm, only a potential idea. I believe it has potential, and especially worth a look given the dearth of useful segmentation algorithms in this domain, but real results would be greatly appreciated in order to truly judge its merits.