Morphometric and Phylogenic Analysis of Six Population Indonesian Local Goats

The research objectives were to characterize morphometric and genetic distance between populations of Indonesian local goats. The morphological discriminant and canonical analysis were carried out to estimate the phylogenic relationship and determine the discriminant variable between Benggala goats (n= 96), Marica (n= 60), Jawarandu (n= 94), (Kacang (n= 217), Muara (n= 30) and Samosir (n= 42). Discriminant analysis used to clasify body weight and body measurements. In the analysis of variance showed that body weight and body measurement (body length, height at withers, thorax width, thorax height, hert girth, skull width and height, tail length and width, ear length and width) of Muara goats was higher (P<0.05) compared to the other groups, and the lowest was in Marica goats. The smallest genetic distance was between Marica and Samosir (11.207) and the highest were between Muara and Benggala (255.110). The highest similarity between individual within population was found in Kacang (99.28%) and the lowest in Samosir (82.50%). The canonical analysis showed high correlation on canon circumference, body weight, skull width, skull height, and tail width variables so these six variables can be used as distinguishing variables among population. The result from Mahalonobis distance for phenogram tree and canonical analysis showed that six populations of Indonesian local goats were divided into six breed of goats: the first was Muara, the second was Jawarandu, the third was Kacang, the fourth was Benggala, the fifth was Samosir and the sixth was Marica goats. The diversity of body size and body weight of goats was observed quite large, so the chances of increasing productivity could be made through selection and mating programs

Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching

A convenient way of dealing with image sets is to represent them as points on Grassmannian manifolds. While several recent studies explored the applicability of discriminant analysis on such manifolds, the conventional formalism of discriminant analysis suffers from not considering the local structure of the data. We propose a discriminant analysis approach on Grassmannian manifolds, based on a graphembedding framework. We show that by introducing within class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, the geometrical structure of data can be exploited. Experiments on several image datasets (PIE, BANCA, MoBo, ETH-80)show that the proposed algorithm obtains considerable improvements in discrimination accuracy, in comparison to three recent methods: Grassmann Discriminant Analysis (GDA), Kernel GDA, and the kernel version of Affine Hull Image Set Distance. We further propose a Grassmannian kernel, based on canonical correlation between subspaces, which can increase discrimination accuracy when used in combination with previous Grassmannian kernels

Graph Neural Network Backend for Speaker Recognition

Currently, most speaker recognition backends, such as cosine, linear discriminant analysis (LDA), or probabilistic linear discriminant analysis (PLDA), make decisions by calculating similarity or distance between enrollment and test embeddings which are already extracted from neural networks. However, for each embedding, the local structure of itself and its neighbor embeddings in the low-dimensional space is different, which may be helpful for the recognition but is often ignored. In order to take advantage of it, we propose a graph neural network (GNN) backend to mine latent relationships among embeddings for classification. We assume all the embeddings as nodes on a graph, and their edges are computed based on some similarity function, such as cosine, LDA+cosine, or LDA+PLDA. We study different graph settings and explore variants of GNN to find a better message passing and aggregation way to accomplish the recognition task. Experimental results on NIST SRE14 i-vector challenging, VoxCeleb1-O, VoxCeleb1-E, and VoxCeleb1-H datasets demonstrate that our proposed GNN backends significantly outperform current mainstream methods

Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and extracted features please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda