This paper presents work in progress to extend the two-dimensional (2D) Scale Invariant Feature Transform (SIFT) to a 2.5 dimensional (2.5D) domain. Robust feature descriptors are extracted from range images of human faces and the form of these descriptors are analogous to the structure of Lowe’s 2D SIFT, in which the descriptors comprise a local distribution function of the image gradient orie...

We present a novel method for a feature descriptor called an exact order based descriptor (EOD). The proposed method consists of three steps. First, to resolve ordering ambiguity for pixels of the same intensity, an exact order image is created by changing the discrete intensity into a k-dimensional continuous value. Second, exact order based features are generated globally and locally. Finally...

Descriptors based on orientation histograms are widely used in computer vision. The spatial pooling involved in these representations provides important invariance properties, yet it is also responsible for the loss of important details. In this paper, we suggest a way to preserve the details described by the local curvature. We propose a descriptor that comprises the direction and magnitude of...

We present a feature extraction method for RGB-D data based on k-means clustering that builds on recent work by Coates et al. Using unsupervised learning methods we are able to automatically learn feature responses that combine all available information (color and depth) into one, concise representation. We show that depth information can substantially increase the recognition performance and t...

In the context of content-based image retrieval from large databases, traditional systems typically compute a single descriptor per image based for example on color histograms. The result of a query is in general the images whose descriptors are the closest to the descriptor of the query image. Systems built this way are able to return images that are globally similar to the query image, but ca...

Nowadays, object recognition based on local invariant features is widely acknowledged as one of the best paradigms for object recognition due to its robustness for solving image matching across different views of a given scene. This paper proposes a new approach for learning invariant region descriptor operators through genetic programming and introduces another optimization method based on a h...

A new texture descriptor, called CSG vector, is proposed for image retrieval and image segmentation in this paper. The descriptor can be generated by composing the gradient vectors obtained from the sub-images through a wavelet decomposition of a texture image. By exercising a database containing 2400 images which were cropped from a set of 150 types of textures selected from the Brodatz Album,...

In this paper we propose a generic framework for the optimization of image feature encoders for image retrieval. Our approach uses a triplet-based objective that compares, for a given query image, the similarity scores of an image with a matching and a non-matching image, penalizing triplets that give a higher score to the non-matching image. We use stochastic gradient descent to address the re...