# Advanced Image and Video Retrieval Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/advanced-image-and-video-retrieval-techniques/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the development and evaluation of techniques for extracting, matching, and utilizing local image features for tasks such as object recognition, image retrieval, and scene classification. It covers a wide range of methods including local descriptors, deep learning approaches, binary codes, and cross-modal retrieval techniques. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10627 |
| Works | 208 |

## Topic papers all

Showing 15 of 208.

- [ImageNet: A large-scale hierarchical image database](https://scholariq.org/papers/imagenet-a-large-scale-hierarchical-image-database/)
- [ImageNet Large Scale Visual Recognition Challenge](https://scholariq.org/papers/imagenet-large-scale-visual-recognition-challenge/)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://scholariq.org/papers/visual-genome-connecting-language-and-vision-using-crowdsourced-dense-image/)
- [Deep visual-semantic alignments for generating image descriptions](https://scholariq.org/papers/deep-visual-semantic-alignments-for-generating-image-descriptions/)
- [A Bayesian Hierarchical Model for Learning Natural Scene Categories](https://scholariq.org/papers/a-bayesian-hierarchical-model-for-learning-natural-scene-categories/)
- [3D Object Representations for Fine-Grained Categorization](https://scholariq.org/papers/3d-object-representations-for-fine-grained-categorization/)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with\n Transformers](https://scholariq.org/papers/segformer-simple-and-efficient-design-for-semantic-segmentation-with-n/)
- [One-shot learning of object categories](https://scholariq.org/papers/one-shot-learning-of-object-categories/)
- [Sparse Representation for Computer Vision and Pattern Recognition](https://scholariq.org/papers/sparse-representation-for-computer-vision-and-pattern-recognition/)
- [Deep Recurrent Neural Networks for Hyperspectral Image Classification](https://scholariq.org/papers/deep-recurrent-neural-networks-for-hyperspectral-image-classification/)
- [SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images](https://scholariq.org/papers/snunet-cd-a-densely-connected-siamese-network-for-change-detection-of-vhr-images/)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://scholariq.org/papers/a-comprehensive-survey-of-deep-learning-for-image-captioning/)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://scholariq.org/papers/segformer-simple-and-efficient-design-for-semantic-segmentation-with/)
- [Large-scale image retrieval with compressed Fisher vectors](https://scholariq.org/papers/large-scale-image-retrieval-with-compressed-fisher-vectors/)
- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://scholariq.org/papers/spectralgpt-spectral-remote-sensing-foundation-model/)

## Topic primary papers

Showing 15 of 44.

- [ImageNet: A large-scale hierarchical image database](https://scholariq.org/papers/imagenet-a-large-scale-hierarchical-image-database/)
- [ImageNet Large Scale Visual Recognition Challenge](https://scholariq.org/papers/imagenet-large-scale-visual-recognition-challenge/)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with\n Transformers](https://scholariq.org/papers/segformer-simple-and-efficient-design-for-semantic-segmentation-with-n/)
- [Large-scale image retrieval with compressed Fisher vectors](https://scholariq.org/papers/large-scale-image-retrieval-with-compressed-fisher-vectors/)
- [RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform](https://scholariq.org/papers/rift-multi-modal-image-matching-based-on-radiation-variation-insensitive-feature/)
- [A Comprehensive Performance Evaluation of 3D Local Feature Descriptors](https://scholariq.org/papers/a-comprehensive-performance-evaluation-of-3d-local-feature-descriptors/)
- [Exploiting local features from deep networks for image retrieval](https://scholariq.org/papers/exploiting-local-features-from-deep-networks-for-image-retrieval/)
- [Video copy detection](https://scholariq.org/papers/video-copy-detection/)
- [Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search](https://scholariq.org/papers/content-based-copy-retrieval-using-distortion-based-probabilistic-similarity/)
- [Automatic Image Registration Through Image Segmentation and SIFT](https://scholariq.org/papers/automatic-image-registration-through-image-segmentation-and-sift/)
- [A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets](https://scholariq.org/papers/a-comprehensive-survey-of-image-segmentation-clustering-methods-performance/)
- [Fully convolutional neural networks for polyp segmentation in colonoscopy](https://scholariq.org/papers/fully-convolutional-neural-networks-for-polyp-segmentation-in-colonoscopy/)
- [Logo retrieval with a contrario visual query expansion](https://scholariq.org/papers/logo-retrieval-with-a-contrario-visual-query-expansion/)
- [Two-View 3D Reconstruction for Food Volume Estimation](https://scholariq.org/papers/two-view-3d-reconstruction-for-food-volume-estimation/)
- [Random maximum margin hashing](https://scholariq.org/papers/random-maximum-margin-hashing/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.