# Person Search via Deep Integrated Networks
## 2 Related Work
proposed a cross-view quadratic discriminant analysis (XQDA) to jointly learn the low-dimensional space and distance metric. In the past, most studies proposed for recognition tasks, such as object detection and person ReID, have focused on either the extraction of low and high-level features or the obtaining of a robust classifier. Low-level features are minor details of the image, such as colour, lines, dots, corners, or edges, that can be extracted by a filter (kernel) via the convolution operation SIFT [50] or HOG [51]. High-level features are more abstract and built on low-level features to detect objects and shapes. However, a few works have jointly optimized feature extraction and classification. Deep learning models can simultaneously extract low and high-level features and learn a classifier in a network. In recent years, this has achieved significant success in the computer vision field [16; 17; 18]. For person ReID, deep models can be roughly categorised into three kinds of approaches: Siamese networks [13; 15; 20], classification networks [21; 22; 23], and triplet networks [24; 25]. Siamese networks take an image pair as an input and output a similarity score to determine whether or not the two input images depict the same person. In 2014, [PERSON] et al. [15] were the first to jointly learn the color feature, texture feature and metric in a Siamese network. A given image pair was partitioned into three overlapped horizontal parts and put through a Siamese CNN model to measure the cosine distance for each pair of parts. In 2014, [PERSON] et al. [13] proposed a filter pairing neural network with six layers to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter. In 2015, [PERSON] et al. [14] proposed a deep network by designing two novel layers: a cross-input neighbourhood difference layer to generate the difference maps by subtracting feature values across two view images, and a patch summary layer to spatially integrate these difference maps. [PERSON] et al. [52] incorporated a long short-term memory model into a Siamese network, which could memorize the spatial information of image parts, and thus these image parts could be processed sequentially to enhance the discriminative capability of features. Classification networks [21; 22; 23] regard the person ReID as a multi-class recognition task that considers each person as a separate class and applies CNN to extract the discriminative features on large-scale datasets. [PERSON] et al. [21] designed a pipeline framework to learn deep features with the CNN on multiple domains to improve the performance. The domain-guided dropout algorithm was also proposed to improve the feature learning procedure. [PERSON] et al. [22] proposed ID-discriminative embedding to fine-tune the pre-trained model from ImageNet to obtain the ReID model. Triplet networks can be considered as an extension version of the Siamese network [24]. [PERSON] et al. [53] designed a triplet-based Siamese CNN model, which takes three pedestrian images as input and uses the triplet loss function to train the network. The proposed multi-channel CNN model can jointly learn both the global full-body and local body-parts features for input images. The extracted features were concatenated into a global representation in a fully-connected layer. The authors in [25; 54] also utilized the triplet loss function for network optimization. In 2018, [PERSON] et al. [55; 56] proposed a point-to-set network (P2 SNet) to jointly learn the feature representations and a point-to-set distance metric in a unified manner by utilizing the triplet loss for network training. Although the CNN model can achieve significant improvement regarding the ReID problem, the parameters of the CNN models are complex compared to the number of training samples. An over-fitting problem might also happen, which refers to a model that is too closely fit to the training data and leads to the poor generalization of test data.