Shape retrieval with eigen-CSS search

Abstract

Shape retrieval programs are comprised of two components: shape representation, and matching algorithm. Building the representation on scale space filtering and the curvature function of a closed boundary curve, curvature scale space (CSS) has been demonstrated to be a robust 2D shape representation. The adoption of the CSS image as the default in the MPEG-7 standard, using a matching algorithm utilizing maxima of the CSS image contours, makes this feature of interest perforce. In this paper, we propose a framework in two stages for a novel approach to both representing and matching the CSS feature. Our contribution consists of three steps, each of which effects a profound speedup on CSS image matching. Each step is a well-known technique in other domains, but the proposed concatenation of steps leads to a novel approach to this subject which captures shape information more efficiently and decreases distracting noise. Firstly, using experience derived from medical imaging, we define a set of marginal-sum features summarizing the CSS image. Secondly, the standard algorithm using CSS maxima involves a complicated and time-consuming search, since the zero of arc length is not known in any new contour. Here, we obviate this search via a phase normalization transform in the spatial dimension of the reduced marginal-CSS feature. Remarkably, this step also makes the method rotationand reflection-invariant. Finally, the resulting feature space is amenable to dimension reduction via subspace projection methods, with a dramatic speedup in time, and as well orders of magnitude reduction in space. The first stage of the resultant program, using a general-purpose eigenspace, has class-categorization accuracy compatible with the original contour maxima program. In a second stage, we generate specialized eigenspaces for each shape category, with little extra runtime complexity because search can still be carried out in reduced dimensionality. In a leave-one-out categorization using the MPEG-7 contour database, a classification success rate of 94.1% over 1400 objects in 70 classes is achieved with very fast matching, and 98.6% in the top-2 classes. A leave-all-in test achieves 99.8% correct categorization. The method is rotation invariant, and is simple, fast, and effective.

Towards semantic maps for mobile robots

“…8(6). We use the Eigen-CSS feature extraction method to describe a contour [42], improving the original CSS method by Mokhtarian [10,28]. In particular, our method is robust against contour rotation, as inspired by the work by Drew, Lee and Rova [10].…”

Object Recognition using Isolumes

“…Other techniques are reported in a comprehensive review (Da Fontura Costa and Cesar, 2009) but we judge that none of them springs forward, fully formed, to solve our problem and none is as important in the literature as cartoon creation followed by attempted recognition (Drew et al, 2009). …”

A Composite Descriptor for Shape Image Retrieval

“…Based on the contour Shape retrieval, distance histogram、Boundary Moment [1]、Elastic Matching [2] 、Fourier Descriptors [3][4][5][6][7][8][9][10][11][12] 、Wavelet Descriptors [13][14][15] 、Curvature Scale Space [16][17][18][19] and Shape Contexts [20] are the typical method. But the above method only described the statistical characteristics of the contour points, ignore the contours of the spatial characteristics, which makes the retrieval results have a certain deviation with the physical application.…”