Open source implementation of "A Self-Supervised Descriptor for Image Copy Detection" (SSCD).

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

A demo that implement image registration by matching SIFT descriptors and appling RANSAC and affine transformation.

Pytorch code of "Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning", CVPR 2019.

This repository contains the source code of BEBLID: Boosted Efficient Binary Local Image Descriptor

Combination of Multiple Global Descriptors for Image Retrieval

The 1st Place Solution of the Facebook AI Image Similarity Challenge (ISC21) : Descriptor Track.

🌁 Image retrieval using deep learning of global descriptors.

This is an official implementation for "Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors“ (AAAI 2023))

Pytorch code for Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners

A PyTorch implementation of CGD based on the paper "Combination of Multiple Global Descriptors for Image Retrieval"

Using global feature descriptors and machine learning to perform image classification

Data descriptor and sample notebooks for the Emory Breast Imaging Dataset (EMBED) hosted on the AWS Open Data Program

DALF is a joint image keypoint detector and descriptor for handling non-rigid deformations. It also works great under large rotations.

[AAAI 2023] DarkFeat: Noise-Robust Feature Detector and Descriptor for Extremely Low-Light RAW Images

How to improve a 14% your image matching with only one line of code? BEBLID is the key!

Official repository for the NeurIPS 2024 paper "SuperVLAD: Compact and Robust Image Descriptors for Visual Place Recognition".

Automatic Panoramic Image Stitching using SIFT detector and descriptor, RANSAC algorithm for best-fit homograpy, linear blending

Instance-level image retrieval, used in street-to-shop scene. Leveraging large-scale noisy data, and clean it automatically. Learning R-MAC descriptors using a larger network such as ResNet101. Train with a siamese architecture to optimize a triplet loss.

Implements Vector of Locally Aggregated Descriptors according to Jegou's paper, for TagMe Image Categorization Competition, 2014

Conjoined Triple Deep Network for Learning Local Image Descriptors

Verify size of ISO 9660 image against Volume Descriptor fields

Shape2Vec: semantic-based descriptors for 3D shapes, sketches and images

MultiPoint: Feature detector and descriptor for optical/thermal image alignment

We propose a new method named Random encoding of Aggregated Deep Activation Maps (RADAM) for feature extraction from pre-trained Deep CNNs. The technique consists of encoding the output at different depths of the CNN using a Randomized Autoencoder, producing a single image descriptor

Simple HTML5 tool to cut bitmap font sheet images and generate angelcode descriptors (.fnt files)

Pre-trained models for our CVPR 2016 paper: "Learning Local Image Descriptors With Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions"

Intensity Triangle Descriptor Constructed from High-Resolution Spinning LiDAR Intensity Image for Loop Closure Detection

NetVLAD based weakly supervised learning whole image descriptor with street view data

Matlab implementation, comparision and improvement of Local texture descriptors. This repo demonstrate usage of Local binary pattern (LBP), Local derivative pattern (LDP), Local Tetra pattern (LTrP), Noise Resistant LBP (NR-LBP), Histogram Refinement of Local texture descriptor for Content based image retrieval (CBIR) application.