K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

Summary of Work for Joint Research Interchanges with DARWIN Integrated Product Team

The intent of Stanford University's SciVis group is to develop technologies that enabled comparative analysis and visualization techniques for simulated and experimental flow fields. These techniques would then be made available un- der the Joint Research Interchange for potential injection into the DARWIN Workspace Environment (DWE). In the past, we have focused on techniques that exploited feature based comparisons such as shock and vortex extractions. Our current research effort focuses on finding a quantitative comparison of general vector fields based on topological features. Since the method relies on topological information, grid matching an@ vector alignment is not needed in the comparison. This is often a problem with many data comparison techniques. In addition, since only topology based information is stored and compared for each field, there is a significant compression of information that enables large databases to be quickly searched. This report will briefly (1) describe current technologies in the area of comparison techniques, (2) will describe the theory of our new method and finally (3) summarize a few of the results

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated

Summary of Work for Joint Research Interchanges with DARWIN Integrated Product Team 1998

The intent of Stanford University's SciVis group is to develop technologies that enabled comparative analysis and visualization techniques for simulated and experimental flow fields. These techniques would then be made available under the Joint Research Interchange for potential injection into the DARWIN Workspace Environment (DWE). In the past, we have focused on techniques that exploited feature based comparisons such as shock and vortex extractions. Our current research effort focuses on finding a quantitative comparison of general vector fields based on topological features. Since the method relies on topological information, grid matching and vector alignment is not needed in the comparison. This is often a problem with many data comparison techniques. In addition, since only topology based information is stored and compared for each field, there is a significant compression of information that enables large databases to be quickly searched. This report will briefly (1) describe current technologies in the area of comparison techniques, (2) will describe the theory of our new method and finally (3) summarize a few of the results

Fast, Dense Feature SDM on an iPhone

In this paper, we present our method for enabling dense SDM to run at over 90 FPS on a mobile device. Our contributions are two-fold. Drawing inspiration from the FFT, we propose a Sparse Compositional Regression (SCR) framework, which enables a significant speed up over classical dense regressors. Second, we propose a binary approximation to SIFT features. Binary Approximated SIFT (BASIFT) features, which are a computationally efficient approximation to SIFT, a commonly used feature with SDM. We demonstrate the performance of our algorithm on an iPhone 7, and show that we achieve similar accuracy to SDM

Non-negative mixtures

This is the author's accepted pre-print of the article, first published as M. D. Plumbley, A. Cichocki and R. Bro. Non-negative mixtures. In P. Comon and C. Jutten (Ed), Handbook of Blind Source Separation: Independent Component Analysis and Applications. Chapter 13, pp. 515-547. Academic Press, Feb 2010. ISBN 978-0-12-374726-6 DOI: 10.1016/B978-0-12-374726-6.00018-7file: Proof:p\PlumbleyCichockiBro10-non-negative.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:p\PlumbleyCichockiBro10-non-negative.pdf:PDF owner: markp timestamp: 2011.04.2

Palmprint Recognition by using Bandlet, Ridgelet, Wavelet and Neural Network

Palmprint recognition has emerged as a substantial biometric based personal identification. Tow types of biometrics palmprint feature. high resolution feature that includes: minutia points, ridges and singular points that could be extracted for forensic applications. Moreover, low resolution feature such as wrinkles and principal lines which could be extracted for commercial applications. This paper uses 700nm spectral band PolyU hyperspectral palmprint database. Multiscale image transform: bandlet, ridgelet and 2D discrete wavelet have been applied to extract feature. The size of features are reduced by using principle component analysis and linear discriminate analysis. Feed-forward Back-propagation neural network is used as a classifier. The recognition rate accuracy shows that bandlet transform outperforms others

Comparison of different integral histogram based tracking algorithms

Object tracking is an important subject in computer vision with a wide range of applications – security and surveillance, motion-based recognition, driver assistance systems, and human-computer interaction. The proliferation of high-powered computers, the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis have generated a great deal of interest in object tracking algorithms. Tracking is usually performed in the context of high-level applications that require the location and/or shape of the object in every frame. Research is being conducted in the development of object tracking algorithms over decades and a number of approaches have been proposed. These approaches differ from each other in object representation, feature selection, and modeling the shape and appearance of the object. Histogram-based tracking has been proved to be an efficient approach in many applications. Integral histogram is a novel method which allows the extraction of histograms of multiple rectangular regions in an image in a very efficient manner. A number of algorithms have used this function in their approaches in the recent years, which made an attempt to use the integral histogram in a more efficient manner. In this paper different algorithms which used this method as a part of their tracking function, are evaluated by comparing their tracking results and an effort is made to modify some of the algorithms for better performance. The sequences used for the tracking experiments are of gray scale (non-colored) and have significant shape and appearance variations for evaluating the performance of the algorithms. Extensive experimental results on these challenging sequences are presented, which demonstrate the tracking abilities of these algorithms