# Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image Retrieval
## 1 Introduction  
With the popularization of personal electronic terminals such as smartphones and tablets, the amount of visual data available on the internet has been overgrown in recent years [1]. Correspondingly, the performance of related image retrieval technologies is also improving and becoming more sophisticated [2]. Content-based image retrieval (CBIR) is a type of search technology that aims to find images similar to the image input by users (query image), and it is extensively applied in the real world [3]. For example, search engines of image retrieval functions (e.g., Google ([[http://images.google.it](http://images.google.it)]([http://images.google.it](http://images.google.it)), accessed on 23 February 2023)) and a similar production search of online shopping (e.g., Amazon ([[https://www.amazon.co.jp/](https://www.amazon.co.jp/)]([https://www.amazon.co.jp/](https://www.amazon.co.jp/)), accessed on 23 February 2023)) and eBay ([[https://www.ebay.com/](https://www.ebay.com/)]([https://www.ebay.com/](https://www.ebay.com/)), accessed on 23 February 2023))are examples of such applications. Conventional CBIR methods have exhibited remarkable precision in retrieving images that bear similarities to the query image, as documented in several studies [4; 5; 6; 7]. It is feasible to efficiently and effectively retrieve associated images from a vast database using a single input image. In the last decade, extensive research efforts have been devoted to developing novel theories and models for CBIR, establishing several practical algorithms [8]. However, as the volume of visual data on the web continues to increase, there is a growing imperative to considerusers' subjective preferences during image retrieval to enhance the value of the retrieved data and satisfy the increasing demands of users. For users, whether the retrieval result is appropriate cannot be judged solely by the image content, but also based on the user's preferences. The aforementioned problem is a crucial challenge known as the semantic gap [9], which CBIR has been facing for a long time. Since the query image input by a user is rich in detail and information, it can be challenging for the CBIR model to accurately localize which specific part of the image the user intends to retrieve a similar one from the dataset [10]. Conventional CBIR methods compare the features of the query image with the features of images in the dataset (candidate images) and rank these candidate images according to their similarity to the query image. [PERSON] et al. [11] proposed an approach to extract dominant color descriptors for CBIR. The approach uses a weighting mechanism that assigns more importance to the informative pixels of an image using suitable masks. However, these methods may fail to rank images fitting to the user's preferences with a high rank consistently. To achieve user-oriented image retrieval, one of the possible strategies is to re-order the images of the initial retrieval, satisfying the user's preferences with a higher position. Re-ranking is an approach to re-order the results of initial retrieval using reliable information and typically plays a role in the post-processing step in image retrieval tasks [12]. Re-ranking methods can be classified into two categories based on the information source employed: self-re-ranking and interactive re-ranking [13]. Self-re-ranking approaches aim to improve the accuracy of initial retrieval results by identifying relevant visual patterns from them and re-ordering them based on external information, such as text labels or class information. For example, [PERSON] et al. [14] proposed a new method based on a global model for extracting features from the entire image and a local model for extracting features from individual regions of interest in the image. The results from these two models are then combined using a re-ranking approach, which significantly improves the accuracy of the retrieval results.