24 research outputs found
Place recognition: An Overview of Vision Perspective
Place recognition is one of the most fundamental topics in computer vision
and robotics communities, where the task is to accurately and efficiently
recognize the location of a given query image. Despite years of wisdom
accumulated in this field, place recognition still remains an open problem due
to the various ways in which the appearance of real-world places may differ.
This paper presents an overview of the place recognition literature. Since
condition invariant and viewpoint invariant features are essential factors to
long-term robust visual place recognition system, We start with traditional
image description methodology developed in the past, which exploit techniques
from image retrieval field. Recently, the rapid advances of related fields such
as object detection and image classification have inspired a new technique to
improve visual place recognition system, i.e., convolutional neural networks
(CNNs). Thus we then introduce recent progress of visual place recognition
system based on CNNs to automatically learn better image representations for
places. Eventually, we close with discussions and future work of place
recognition.Comment: Applied Sciences (2018
Estimation of Individual Micro Data from Aggregated Open Data
In this paper, we propose a method of estimating individual micro data from
aggregated open data based on semi-supervised learning and conditional
probability. Firstly, the proposed method collects aggregated open data and
support data, which are related to the individual micro data to be estimated.
Then, we perform the locality sensitive hashing (LSH) algorithm to find a
subset of the support data that is similar to the aggregated open data and then
classify them by using the Ensemble classification model, which is learned by
semi-supervised learning. Finally, we use conditional probability to estimate
the individual micro data by finding the most suitable record for the
probability distribution of the individual micro data among the classification
results. To evaluate the performance of the proposed method, we estimated the
individual building data where the fire occurred using the aggregated fire open
data. According to the experimental results, the micro data estimation
performance of the proposed method is 59.41% on average in terms of accuracy.Comment: 7 page
Accurate fashion and accessories detection for mobile application based on deep learning
Detection and classification have an essential role in the world of e-commerce applications. The recommendation method that is commonly used is based on information text attached to a product. This results in several recommendation errors caused by invalid text information. In this study, we propose the development of a fashion category (FC-YOLOv4) model in providing category recommendations to sellers based on fashion accessory images. The resulting model was then compared to YOLOv3 and YOLOv4 on mobile devices. The dataset we use is a collection of 13,689, which consists of five fashion categories and five accessories' categories. Accuracy and speed analysis were performed by looking at mean average precision (mAP) values, intersection over union (IoU), model size, loading time, average RAM usage, and maximum RAM usage. From the experimental results, an increase in mAP was obtained by 99.84% and an IoU of 88.49 when compared to YOLOv3 and YOLOv4. Based on these results, it can be seen that the models we propose can accurately identify fashion and accessories categories. The main advantage of this paper lies in i) providing a model with a high level of accuracy and ii) the experimental results presented on a smartphone