in this article three retrieval models for probabilistic indexing are described along with evaluation results for each. First is the binary independence indexing @II) model, which is a generalized version of the Maron and Kuhns indexing model. In this model, the indexing weight of a descriptor in a document is an estimate of the probability of relevance of this document with respect to queries ...

Stereo matching between images is at the base of many computer vision applications. Many traditional image features for matching focus on being robust to view-point changes with huge amount of calculation and memory storage, such as Scale Invariant Feature Transform (SIFT). Binary Robust Independent Elementary Features (BRIEF) is an efficient alternative to traditional SIFT-like features, but i...

Despite the great success achieved by prevailing binary local descriptors, they are still suffering from two problems: 1) vulnerable to geometric transformations; 2) lack of an effective treatment highly-correlated bits that generated directly applying scheme image hashing. To tackle both limitations, we propose unsupervised Transformation-invariant Binary Local Descriptor learning method (TBLD...

This paper proposes a texture analysis of the printed document based on Local Binary Pattern (LBP) descriptor for the application of printer identification. The LBP provides a statistical description of the pixels’ gray level differences within their neighborhoods. The occurrence histogram of local binary patterns is able to capture the document’s texture modifications by the distortion during ...

In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbou...

A new method (particularly suited to the analysis of High Throughput Screening data) is presented for the determination of quantitative structure activity relationships. The method, termed "Binary QSAR," accepts binary activity measurements (e.g., pass/fail or active/inactive) and molecular descriptor vectors as input. A Bayesian inference technique is used to predict whether or not a new compo...

In this paper, we propose a novel feature type to recognize human actions from video data. By combining the benefit of Volume Local Binary Patterns and Optical Flow, a simple and efficient descriptor is constructed. Motion Binary Patterns (MBP) are computed in spatio-temporal domain while static object appearances as well as motion information are gathered. Histograms are used to learn a Random...

Abstract. Binary features have received much attention with regard to memory and computational efficiency with the emerging demands in the mobile and embedded vision systems fields. In this context, we present a robust binary feature using the intensity order. By analyzing feature regions, we devise a simple but effective strategy to detect keypoints. We adopt an ordinal description and encode ...