# Person Search via Deep Integrated Networks
## 2 Related Work  
Person ReID is a difficult problem caused by larger appearance variations that has been gaining a great deal of attention for decades. The mainstream approaches used to solve this problem are either to extract discriminative features or design a distance metric to measure the similarity between images. Feature representation aims to extract robust and discriminative features that are invariant to the appearance variations. Most of the previous studies have focused on hand-crafted features that are designed beforehand by human experts to extract a given set of chosen characteristics. [PERSON] et al. [42] proposed an ensemble of localised features by extracting color and spatial features to address the viewpoint problem. [PERSON] et al. [43] extracted three kinds of features--namely the weighted color histogram, maximally stable color regions, and recurrent high-structured patches--to overcome appearance variations. However, it is time-consuming to extract these three types of features [44]. Earlier studies [42; 43; 45] extracted the color features in the RGB or HSV space, and the performance was limited for lighting or illumination variations. In [44; 46; 47], the authors applied color names which were intended to relate the numerical colours in the RGB or HSV space with the corresponding semantic color names used in natural language [46] and showed that the feature of color names was more stable for illumination variations. [PERSON] et al. also proposed the salient color names-based color descriptor (SCNCD) [44], and 16 salient colors were applied. Each image was represented as the distribution of salient colors for further similarity measurement. The SCNCD is one representative work among the early methods of extracting color features. For the similarity measurement, metric learning [12; 40; 41; 48; 49] aims to learn a \(d\times d\) matrix to calculate the Mahalanobis distance between two images, each of which is represented as a \(d\)-dimensional vector of hand-crafted features. In [40], [PERSON] et al. proposed a relative distance comparison and an applied logistic function to minimize the distance between the \"same\" pairs whilst maximizing the distance between \"different\" pairs. [PERSON] et al. [48] considered the relation of triplets to exploit the local structure of data in the feature space. For each training datum, a local perimeter in the feature space was established by the target neighbors--i.e., the k-nearest neighbors of a datum with the same label--and the impostors are penalised. The impostor is a datum that has smaller distance to the datum than the distance between the datum and its closet target neighbour. To learn the distance metric, the objective function with the form of a mathematical equation was designed to minimize the distance between the target neighbors and the penalisation of the impostors. [PERSON] et al. [41] presented an information-theoretic approach, called ITML, to learn the Mahalanobis distance metric. In contrast to [40; 48], in which eigenvalue decomposition or semi-definite programming is applied to obtain the distance matrix, the optimization of ITML is fast and scalable [41]. Considering the growing amount of data, learning a metric on a large dataset raises the problem of providing fully supervised labels for all data points [12]. Hence, in [12], [PERSON] et al. proposed the KISS method and provided data labels in the form of equivalence constraints. Inspired by the statistical inference, a simple and effective strategy was introduced to learn a distance metric with a matrix form from the equivalence constraints based on a likelihood-ratio test. The number of parameters in the matrix is large, and principal component analysis (PCA) is first applied to reduce the dimension of the feature vector. Then KISS method is performed to learn the metric in the low-dimensional PCA space [11]. However, PCA is not jointly optimized with the metric learning, and the performance is limited. Hence, in [11], [PERSON] et al.