Image and video face retrieval with query image using convolutional neural network features

Abstract

This paper addresses the issue of image and video face retrieval. The aim of this work is to be able to retrieve images and/or videos of specific person from a dataset of images and videos if we have a query image of that person. The methods proposed so far either focus on images or videos and use hand crafted features. In this work we built an end-to-end pipeline for both image and video face retrieval where we use convolutional neural network (CNN) features from an off-line feature extractor. And we exploit the object proposals learned by a region proposal network (RPN) in the online filtering and re-ranking steps. Moreover, we study the impact of finetuning the networks, the impact of sum-pooling and max-pooling, and the impact of different similarity metrics. The results that we were able to achieve are very promising.

An image-based convolutional neural network system for road defects detection

“…Object and features detection took a huge place in computer vision these last decades and on of its most boost is the emergence of convolutional neural networks (CNN), mainly by providing a better performance in visualizing and classifying images [2]. They have led to a rapid development of different fields of studies (identification [3], [4], medical [5], [6], automotive [7], and [8]). In the field of object detection, the most recent cutting-edge algorithms are faster region based CNN (Faster R-CNN) [3], [9]- [12], single shot multibox detector (SSD) [11], [13], [14] and you only look once (YOLO) [10], [11], [15].…”

“…They have led to a rapid development of different fields of studies (identification [3], [4], medical [5], [6], automotive [7], and [8]). In the field of object detection, the most recent cutting-edge algorithms are faster region based CNN (Faster R-CNN) [3], [9]- [12], single shot multibox detector (SSD) [11], [13], [14] and you only look once (YOLO) [10], [11], [15]. This last one, as a deep learning-based model, have been considered to be the most efficient approach to detecting objects and characteristics such as surface cracks [16], but also a strong and competitive approach for detecting potholes [11].…”