Encountering Mandela on Screen： Transnational Collaboration in Mandela Image Production From 1987-2010

This article examines transnational collaboration in the production of Mandela＇s biopics and what it means for Mandela＇s self-image, for South African history, and for the globalization of human experience. The films covered in this discussion include： Mandela （1987）, Sarafina （1992）, Mandela and de Klerk （1997）, Goodbye Bafana （2007）, Endgame （2009）, and Invictus （2010）. The article examines the portrayals of Mandela as a lover, action hero, conciliator, and as a symbol of the anti-apartheid liberation struggle, and then focuses on Invictus, Clint Eastwood＇s adaptation of Carlin＇s book, Playing the Enemy to show the degree to which Mandela is incarnated on screen through Morgan Freeman＇s stunning performance while at the same time underscoring the dangers of Euro-American cultural production of Mandela＇s image. While Carlin＇s book employs reminiscences, flashbacks, and shifting chronology to expose the injustice, oppression and brutality that the Springboks symbolized, the film instead focuses on magnifying Mandela＇s image and charm at the expense of South African history, leading to the misrepresentation of Mandela： the ＂heroic self-transcendence＂ typical of Hollywood＇s shallow treatment of historical material for commercial and cultural expediency.

Product image sentence annotation based on kernel descriptors and tag-rank

Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.

AUTOMATIC FAST CLASSIFICATION OF PRODUCT-IMAGES WITH CLASS-SPECIFIC DESCRIPTOR

To achieve online automatic classification of product is a great need of e-commerce de-velopment. By analyzing the characteristics of product images, we proposed a fast supervised image classifier which is based on class-specific Pyramid Histogram Of Words (PHOW) descriptor and Im-age-to-Class distance (PHOW/I2C). In the training phase, the local features are densely sampled and represented as soft-voting PHOW descriptors, and then the class-specific descriptors are built with the means and variances of distribution of each visual word in each labelled class. For online testing, the normalized chi-square distance is calculated between the descriptor of query image and each class-specific descriptor. The class label corresponding to the least I2C distance is taken as the final winner. Experiments demonstrate the effectiveness and quickness of our method in the tasks of product clas-sification.

PRODUCT IMAGE RETRIEVAL BASED ON CO-FEATURES OF THE OBJECT

In this paper, we propose a product image retrieval method based on the object contour corners, image texture and color. The product image mainly highlights the object and its background is very simple. According to these characteristics, we represent the object using its contour, and detect the corners of contour to reduce the number of pixels. Every corner is described using its approximate curvature based on distance. In addition, the Block Difference of Inverse Probabilities (BDIP) and Block Variation of Local Correlation (BVLC) texture features and color moment are extracted from image's HIS color space. Finally, dynamic time warping method is used to match features with different length. In order to demonstrate the effect of the proposed method, we carry out experiments in Mi-crosoft product image database, and compare it with other feature descriptors. The retrieval precision and recall curves show that our method is feasible.

Evapotranspiration Estimation Based on MODIS Products and Surface Energy Balance Algorithms for Land(SEBAL) Model in Sanjiang Plain,Northeast China

In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.

Automatic Product Image Classification with Multiple Support Vector Machine Classifiers

For the task of visual-based automatic product image classification for e-commerce,this paper constructs a set of support vector machine(SVM) classifiers with different model representations.Each base SVM classifier is trained with either different types of features or different spatial levels.The probability outputs of these SVM classifiers are concatenated into feature vectors for training another SVM classifier with a Gaussian radial basis function(RBF) kernel.This scheme achieves state-of-the-art average accuracy of 86.9%for product image classification on the public product dataset PI 100.

Examining Mediating Role of Customer Loyalty for Influence of Brand Related Attributes on Customer Repurchase Intention

The objective of current study was to investigate the influence of brand related attributes on customer loyalty and repurchase intention in agricultural product marketing in Pakistan. Results of current study would help the management of branded agricultural products, in Pakistan specifically and in developing countries generally, to formulate the best strategies to enhance customer loyalty in order to induce repurchase intention of them. Future research could be conducted in the same field by adopting larger sample size and this research can be replicated with specific product category.