This paper describes a strategy to visualise a data-set of multi-component feature vectors using multidimensional scaling (MDS). MDS is employed, instead of more commonly applied mapping techniques, as
it can utilise arbitrary distance measures and hence can easily incorporate the non-linear distance metrics employed when matching multi-component vectors. To test this mapping approach, we have applied it to a data-set of two hundred and sixty eight images, segmented into multiple components each represented by a shape descriptor. The inter-image distances are measured using a series of simple non-location based image distance metrics. The maps are encouraging, with well clustered areas for duplicate or near duplicate trademarks. This gives a clear indication that MDS can be used for this type of visualisation task. However, the maps themselves
also significantly highlight the inadequacies of the segmentation and matching phases. Particularly for the images with an overall impression that doesn’t correspond to the segmented parts, for example figure/ground reversal or macro texture.
Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.
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