Relevance feature mapping for content-based multimedia information retrieval

This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.     

Privacy enabled video surveillance using a two state Markov tracking algorithm

Nowadays video surveillance systems are widely deployed in many public places. However, the widespread use of video surveillance violates the privacy rights of the people. Many authors have addressed the privacy issues from various points of view. In this paper we propose a novel, on-demand selectively revocable, privacy preserving mechanism. The surveillance video can be tuned to view with complete privacy or by revoking the privacy of any subset of pedestrians while ensuring complete privacy to the remaining pedestrians. We achieve this by tracking the pedestrians using a novel Markov chain algorithm with two hidden states, detecting the head contour of the tracked pedestrians and obscuring their faces using an encryption mechanism. The detected pedestrian face/head is obscured by encrypting with a unique key derived from a master key for the privacy preservation purpose. The performance evaluations on many challenging surveillance scenarios show that the proposed mechanism can effectively and robustly track as well as identify multiple pedestrians and obscure/unobscure their faces/head in real time.     

Characteristics of an immobilized lipase for the commercial synthesis of esters

The lipase-catalyzed synthesis of ester bonds has been well-documented lately and is of much current commercial interest. Immobilization of a fungal lipase on a unique macroporous support allows not only the ability to operate in non-aqueous media but to catalyze ester synthesis in quantitative yields, employing attractive commercial conditions. Catalyst dose and process configurations will be illustrated. The capability of the catalyst to operate efficiently in reverse under a variety of unnatural, hostile, solvent-containing environments will be discussed. The range of substrates for this immobilized lipase, Lipzome, has been investigated. The enzyme will catalyze ester synthesis with saturated, unsaturated and a variety of branched carboxylic acids. The alcohol specificity for this enzyme also is equally broad. A wide variety of straight-chain, branched and polar alcohols can be substrates. In addition, some examples of alcohol specificity for kinetic isomer resolution will be cited. Presented at the symposium “The Biology, Biotechnology and Technology of Lipases” at the 78th annual meeting of the American Oil Chemists’ Society held May 17–21, 1987, in New Orleans, Louisiana.     

Remotely sensed indicators of habitat heterogeneity: Use of synthetic aperture radar in mapping vegetation structure and bird habitat

An integrated remote sensing/field ecology project linked the use of synthetic aperture radar (SAR) and aerial photography to studies of landscape spatial heterogeneity and bird community ecology. P-, L-, and C-band SAR data, collected over a section of Kakadu National Park in Australia's Northern Territory during the Joint NASA/Australia DC-8 data acquisition campaign, were analyzed in light of field data integrating vegetation structure and floristics with bird abundances across a heterogeneous study site. Results indicate that SAR data are able to discern structural differences relevant to bird habitat quality within floristically homogeneous stands, while multispectral sensors successfully identified floristic differences among habitat types. Simplifying indices of bird diversity showed ambiguous changes across the site; however, the abundances of individual species were observed to change significantly across both floristic and structural gradients. These results suggest that efforts to map bird diversity should focus on species-specific habitat relationships and that some measure of vegetation structure is needed to understand bird habitat. The approach employed here advances the use of SAR data in the three-dimensional mapping of animal habitats from remotely sensed data, and extends current capabilities for mapping and modeling large-scale patterns in the distribution of biological diversity.     

Computing planar swept polygons under translation

A translational sweep is the translating of a polygon, called the generatrix G, around another polygon, called the directrix D, under two conditions: (1) G is always in contact with D; and (2) the interiors of G and D do not intersect. Three classes of translational sweep are studied, including the case in which both G and D are convex; the case in which G is convex, D monotone; and the case in which both are monotone. Efficient algorithms for computing the trajectory and the swept area as well as geometric and computational properties are presented for each class. A notion called the inverse generatrix, which reveals a duality between the trajectory and the swept polygon, is introduced to reduce complexity.     

The Challenges of Cross-Modal Translation: English-to-Sign-Language Translation in the Zardoz System

The sign languages used by deaf communities around the world represent a linguistic challenge that natural-language researchers in AI have only recently begun to take up. This challenge is particularly relevant to research in Machine Translation (MT), as natural sign languages have evolved in deaf communities into efficient modes of gestural communication, which differ from English not only in modality but in grammatical structure, exploiting a higher dimensionality of spatial expression. In this paper we describe Zardoz, an on-going AI research system that tackles the cross-modal MT problem, translating English text into fluid sign language. The paper presents an architectural overview of Zardoz, describing its central blackboard organization, the nature of its interlingual representation, and the major components which interact through this blackboard both to analyze the verbal input and generate the corresponding gestural output in one of a number of sign variants.     

Benchmarking Least Squares Support Vector Machine Classifiers

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.     

Assessing Adaptive Expertise in Undergraduate Biomechanics

This paper describes the design, development, implementation, and assessment of a multimedia‐based learning module focused on biomechanics. The module is comprised of three challenges and is based on a model of learning and instruction known as the How People Learn (HPL) framework. Classroom assessment of the first challenge was undertaken to test the hypothesis that the HPL approach increases adaptive expertise in movement biomechanics. Student achievement was quantified using pre‐ and post‐test questionnaires designed to measure changes in three facets of adaptive expertise: factual and conceptual knowledge and transfer. The results showed that the HPL approach increased students' conceptual knowledge as well as their ability to transfer knowledge to new situations. These findings indicate that challenge‐based instruction, when combined with an intellectually engaging curriculum and principled instructional design, can accelerate the trajectory of novice to expert development in bioengineering education.