12301513
A Comparative Study of Skin-Color Models
In this paper, we report the results of a comparative study on skin-color models generally used for facial region location. These include two 2D Gaussian models developed in normalized RGB and HSV color spaces respectively, a 1D lookup table model of hue histogram, and an adaptive 3D threshold box model. Also, we present a new model – called “adaptive hue lookup table”. The model is developed by introducing the so-called “Continuously Adaptive Mean Shift” (Camshift) technique into a traditional hue lookup table method. With the introduction of Camshift, the lookup table is able to adaptively adjust its parameters to fit the illumination conditions of different test images. In the experiments reported here, we compare the proposed method with the four typical skin-color filters in the scenarios of different human races and illuminations. The obtained results indicate that the proposed method reaches the best balance between false detection and detect rate.
3104346
Selecting discriminant eigenfaces for face recognition
In realistic face recognition applications, such as surveillance photo identification, supervised learning algorithms usually fail when only one training sample per subject is available. The lack of training samples and the considerable image variations due to aging, illumination and pose variations, make recognition a challenging task. This letter proposes a development of the traditional eigenface solution by applying a feature selection process on the extracted eigenfaces. The proposal calls for the establishment of a feature subspace in which the intrasubject variation is minimized while the intersubject variation is maximized. Extensive experimentation following the FERET evaluation protocol suggests that in the scenario considered here, the proposed scheme improves significantly the recognition performance of the eigenface solution and outperforms other state-of-the-art methods.
2749844
Image and Video Segmentation by Anisotropic Kernel Mean Shift
Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particularly video data. In this paper we present an anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the local structure of the image or video. We decompose the anisotropic kernel to provide handles for modifying the segmentation based on simple heuristics. Experimental results show that the anisotropic kernel mean shift outperforms the original mean shift on image and video segmentation in the following aspects: 1) it gets better results on general images and video in a smoothness sense; 2) the segmented results are more consistent with human visual saliency; 3) the algorithm is robust to initial parameters.
6163740
SEP: a space efficient pipelining technique for managing disk buffers in multimedia servers
We present techniques for managing disks as a buffer for the tertiary storage of multimedia servers. A unified disk space is used to not only cache frequently referenced objects, but also to provide staging buffers for playing back objects. A new staging technique and three buffer replacement policies are investigated. The simulation results indicate that the staging method is very efficient, and each of the replacement policies offers significant additional improvement. Some of the performance issues are also analyzed analytically. The mathematical model provides guidelines for good multimedia systems design using the proposed techniques.
13574031
Mining genes in DNA using GeneScout
In this paper we present a new system, called GeneScout, for predicting gene structures in vertebrate genomic DNA. The system contains specially designed hidden Markov models (HMMs) for detecting functional sites including protein-translation start sites, mRNA splicing junction donor and acceptor sites, etc. Our main hypothesis is that, given a vertebrate genomic DNA sequence S, it is always possible to construct a directed acyclic graph G such that the path for the actual coding region of S is in the set of all paths on G. Thus, the gene detection problem is reduced to that of analyzing the paths in the graph G. A dynamic programming algorithm is used to find the optimal path in G. The proposed system is trained using an expectation-maximization (EM) algorithm and its performance on vertebrate gene prediction is evaluated using the 10-way cross-validation method. Experimental results show the good performance of the proposed system and its complementarity to a widely used gene detection system.
2286283
Query result ranking over e-commerce web databases
To deal with the problem of too many results returned from an E-commerce Web database in response to a user query, this paper proposes a novel approach to rank the query results. Based on the user query, we speculate how much the user cares about each attribute and assign a corresponding weight to it. Then, for each tuple in the query result, each attribute value is assigned a score according to its "desirableness" to the user. These attribute value scores are combined according to the attribute weights to get a final ranking score for each tuple. Tuples with the top ranking scores are presented to the user first. Our ranking method is domain independent and requires no user feedback. Experimental results demonstrate that this ranking method can effectively capture a user's preferences.
8013928
OMAR: Utilizing Multiuser Diversity in Wireless Ad Hoc Networks
One of the most promising approaches to improving communication efficiency in wireless communication systems is the use of multiuser diversity. Although it has been widely investigated and shown feasible and efficient in cellular networks, there is little work for the ad hoc networks, especially in real protocol and algorithm design. In this paper, we propose a novel scheme, namely, the opportunistic medium access and auto rate (OMAR), to efficiently utilize the shared medium in IEEE 802.11-based ad hoc networks by taking advantage of diversity, distributed scheduling, and adaptivity. In an ad hoc network, especially in a heterogeneous ad hoc network or a mesh network, some nodes may need to communicate with multiple one-hop nodes. We allow such a node with a certain number of links to function as a clusterhead to locally coordinate multiuser communications. We introduce a CDF-based (cumulative distribution function) K-ary opportunistic splitting algorithm and a distributed stochastic scheduling algorithm to resolve intra and intercluster collisions, respectively. Fairness is formulated and solved in terms of social optimality within and across clusters. Analytical and simulation results show that our scheme can significantly improve communication efficiency while providing social fairness