Toward content-based object recognition with image primitive

Guisong Wang; Jason Kinser

doi:10.1117/12.646436

16 February 2006 Towards content based object recognition with image primitive

Guisong Wang, Jason Kinser

Proceedings Volume 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning; 60641H (2006) https://doi.org/10.1117/12.646436
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

Content-based object recognition is very useful in many applications, such as medical image processing and diagnosis, target identification with satellite remote sensing. For content-based object recognition, the representation of image segments is critical. Although there are already some approaches to represent image shapes, many of them have limitations because of their insensitivity to the deviations of object appearance. In this paper, an approach is proposed by constructing an image primitive database and representing image with a basis set extracted from the database. The cortical modeling is used here to extract the basis set by isolating the inherent shapes within each image from an image database and defines shapes from this basis set. In our approach, image segments are clustered based on similarity in perimeter and size instead of centroid based metrics by employing the fractional power filter, and the clusters are represented in descriptive vectors as signatures and form basis for shape representation. This approach has advantages in sensitivity to the idiosyncratic nature of the distribution of shapes and efficiency. For validation, we selected a large number of images from web sites randomly. The experiments indicate that describing shapes from this basis set is robust to alterations of the shape such as small occlusions, limited skew, and limited range.

Citation Download Citation

Guisong Wang and Jason Kinser "Towards content based object recognition with image primitive", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60641H (16 February 2006); https://doi.org/10.1117/12.646436

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