Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

Abstract

Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments.

A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras

“…It has been widely used in carriers, such as robots. In [42][43][44][45][46][47][48], the latest image feature extraction and image retrieval technologies were discussed, along with an analysis of the state-of-the-art methods for image location recognition, using deep learning and visual positioning based on traditional image features. It was concluded that the positioning success rate of neural network models based on deep-learning training needs to be improved and that it is difficult for the positioning accuracy to reach the decimeter level.…”

Special Issue on Visual Sensors

“…The proposed descriptor comprises two histograms jointly: a perceptual uniform histogram and a motif co-occurrence histogram including the probability of a pair of motif patterns. Finally, García-Olalla et al [13] proposed a method for textile based image retrieval for indoor environments based on describing the images with different channels (RGB, HSV, etc.) and using the combination of two different descriptors for the image.…”

Intelligent Water Drop Algorithm based Relevant Image Fetching using Histogram and Annotation Features

“…In [15] writer attempted texture based image retrieval (TBIR) utilizing machine learning algorithms and their amalgamation including Faster Region based Convolution Neural Network (R-CNN), Adaptive Linear Binary Pattern (ALBP), Complete Local Oriented Statistical Information Booster (CLOSIB) Histogram of Oriented Gradients (HOG) and Half Complete Local Oriented Statistical Information Booster (HCLOSIB) for neighboring patch explanation of clothing. Their dataset is collected of 684 pictures of sizes that series between 480x360 and 1280x720 pixels gathered from 15 videos of YouTube.…”