Visual Categorisation in a Modified Self Organising Map: Implications for Category-specific Agnosia
نویسندگان
چکیده
1. ABSTRACT There are many reports of patients who, after sustaining brain damage, exhibit a selective recognition deficit for certain categories of object. There has been much controversy as to whether this is informative about the neural organisation of knowledge in the human brain. In this paper we describe an unsupervised neural network model that is trained to process images from a variety of different object categories. Analysis of the unsupervised representations reveals some interesting distinctions between different classes of object. We contend that this model indicates a natural perceptual distinction between certain object categories, which may become exaggerated by the effects of human brain damage.
منابع مشابه
Unsupervised object discovery via self-organisation
Object discovery in visual object categorisation (VOC) is the problem of automatically assigning class labels to objects appearing in given images. To achieve stateof-the-art results in this task, a large set of positive and negative training images from publicly available benchmark data sets have been used to train discriminative classification methods. The immediate drawback of these methods ...
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