Optimal Geometric Matching for Patch-Based Object Detection

We present an efficient method to determine the optimal matching of two patch-based image object representations under rotation, scaling, and translation (RST). This use of patches is equivalent to a fullyconnected part-based model, for which the presented approach offers an efficient procedure to determine the best fit. While other approaches that use fully connected models have a high complexity in the number of parts used, we achieve linear complexity in that variable, because we only allow RST-matchings. The presented approach is used for object recognition in images: by matching images that contain certain objects to a test image, we can detect whether the test image contains an object of that class or not. We evaluate this approach on the Caltech data and obtain very competitive results

Object Recognition Using Segmentation for Feature Detection

A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a set of typical regions, and use a new segmentation method- “Similarity-Measure Segmentation ”- to split the im-ages into regions of interest. This approach may also deliver segments, which are split into several disconnected parts, which turns out to be a powerful description of local similarities. Several textu-ral features are calculated for each region, which are used to learn object categories with Boosting. We demonstrate the flexibility and power of our method by excellent results on various datasets. In comparison, our recognition results are significantly higher than results published in related work.

Object Recognition Using Segmentation for Feature Detection

A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a set of typical regions, and use a new segmentation method- “Similarity-Measure Segmentation ”- to split the images into regions of interest. This approach may also deliver segments, which are split into several disconnected parts, which turns out to be a powerful description of local similarities. Several textural features are calculated for each region, which are used to learn object categories with Boosting. We demonstrate the flexibility and power of our method by excellent results on various datasets. In comparison, our recognition results are significantly higher than results published in related work. 1