# Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
## 2 Related Work  
Automatic image annotation (AIA) methods can roughly be categorized into two categories, global- and local-based methods. Global-based AIA methods, such as [8; 10; 11; 23], are not able to correctly assign important semantic concepts, since the properties and semantics of distinct regions are not often taken into account. As a result, local-based techniques have emerged to overcome this challenge by attempting to capture semantics at the region level rather than holistically. In this section, we review works that attempt to solve the problem of AIA at the region level. [PERSON] et al. [24] used a hierarchical model based on Gaussian mixtures to link low-level visual characteristics and then estimated the shared density of visual characteristics on the regions with semantic notions. Strict semantic constraints were imposed on training data to ensure that each keyword is considered as a category. As a result, areas with similar semantic content are divided into groups based on their content similarity. [PERSON] et al. [25] proposed three hierarchical probabilistic mixture models, culminating in the Corr-LDA model, for image annotation in which the joint probabilities between words and regions are estimated. Later on, the Corr-LDA model was improved in [26] by the addition of a class variable above the mixing proportion parameter of the former model. In the improved model, the general scene is classified, each item is recognized and segmented, and the image is marked with a label list. Another approach to tackling the issue of AIA is by considering the region-concept or concept-concept co-occurrence. [PERSON] et al. [27] first uniformly divide the image into NxM regular grid and then perform vector quantization of the subimages. This leads to results showing that each subimage may be associated with a collection of labels picked from words allocated to the entire image. One major drawback of this model is the need for a large number of training samples to estimate the appropriate likelihood. It also tends to assign repeated words to the same subimage. Inspired by concept-image co-occurrence matrix and machine translation models, the cross-media relevance model [28] emergedand demonstrated the efficiency of learning the codistribution of blobs and keywords. Blobs, in this context, are a result of clustering image features extracted from regions after using some typical segmentation algorithm. Instead of modeling blob-keyword via simple correlation, authors in [29] modeled word probabilities using a multiple Bernoulli model and image feature probabilities using a nonparametric kernel density. In [30], authors proposed a label co-occurrence learning framework based on graph convolution networks (GCNs) to directly examine the dependencies between pathologies for the multilabel chest X-ray. The aforementioned works require large numbers of training samples and have limited generalization ability to new categories. [PERSON] et al. [31] introduced a multilabel few-shot model for general image recognition. It first correlates different labels, based on statistical label co-occurrences, using a structured knowledge graph. The graph is then exploited via network propagation, enabling the learning of contextualized image feature representations. [PERSON] et al. [32] regarded the problem of AIA as analogous to machine translation in which one representation form (i.e., region) is desired to be translated to another (i.e., word). By opting for such a model, the correspondence region-label can easily be modeled via a conventional EM algorithm. Thereafter, the authors presented two classes of models for the joint distribution of text-blob and showed how they are applied image annotation [33]  
Some other attempts for AIA have been accomplished using machine learning techniques. In [13], images are segmented into regions from which the visual features are extracted and then used to train a new asymmetrical support vector machine-based MIL algorithm (ASVM-MIL).