Each object in a digital image is composed of many patches (segments) with different shapes and colors. In order to recognize an object, e.g. a table or a book, it is necessary to find out which segments are typical for which object and in which segment neighborhood they occur. If a typical segment in a characteristic neighborhood is found, this segment will be part of the object to be recognized. Typical adjacent segments for a certain object define the whole object in the image. Following this idea, we introduce a procedure that learns typical segment configurations for a given object class by training with example images of the desired object, which can be found in and downloaded from the Internet. The procedure employs methods from machine learning, namely k-means clustering and decision trees, and from computer vision, e.g. contour signatures.
Paper
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Semantic Object Recognition using Clustering and Decision Trees
Semantic Scholar · Computer Science · 2011
Abstract
Each object in a digital image is composed of many patches (segments) with different shapes and colors. In order to recognize an object, e.g. a table or a book, it is necessary to find out which segments are typical for which object and in which segment neighborhood they occur. If a typical segment in a characteristic neighborhood is found, this segment will be part of the object to be recognized. Typical adjacent segments for a certain object define the whole object in the image. Following this idea, we introduce a procedure that learns typical segment configurations for a given object class by training with example images of the desired object, which can be found in and downloaded from the Internet. The procedure employs methods from machine learning, namely k-means clustering and decision trees, and from computer vision, e.g. contour signatures.