Encoding Classes of Unaligned Objects Using Structural Similarity Cross-Covariance Tensors

Encoding an object essence in terms of self-similarities between its parts is becoming a popular strategy in Computer Vision. In this paper, a new similarity-based descriptor, dubbed Structural Similarity Cross-Covariance Tensor is proposed, aimed to encode relations among different regions of an image in terms of cross-covariance matrices. The latter are calculated between low-level feature vectors extracted from pairs of regions. The new descriptor retains the advantages of the widely used covariance matrix descriptors [1], extending their expressiveness from local similarities inside a region to structural similarities across multiple regions. The new descriptor, applied on top of HOG, is tested on object and scene classification tasks with three datasets. The proposed method always outclasses baseline HOG and yields significant improvement over a recently proposed self-similarity descriptor in the two most challenging datasets

Performance evaluation of local features for object discovery

2015 Spring.Includes bibliographical references.Object recognition is one of the most challenging tasks in computer vision. A common approach in recognizing an object begins by detecting local features in image using a feature detector and describing detected features in terms of feature vectors using a feature descriptor. Many local feature detectors and feature descriptors have been proposed in literature. This work evaluates performance of two successful feature detectors and five feature descriptors on three datasets with unique characteristics. Based on the information content in a given dataset we find general trends on the performance of local features. Our findings will guild computer vision practitioners selecting between alternative local feature detector and local feature descriptor to design highly accurate recognition systems