A Dense-depth Representation For VLAD Descriptors In Content-based Image Retrieval

Abstract

The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural Network (CNN), it is possible to obtain a hierarchy of features from the evaluated image. At every step, the patches extracted are smaller than the previous levels and more representative. Following this idea, this paper introduces a new detector applied on the feature maps extracted from pre-trained CNN. Specifically, this approach lets to increase the number of features in order to increase the performance of the aggregation algorithms like the most famous and used VLAD embedding. The proposed approach is tested on different public datasets: Holidays, Oxford5k, Paris6k and UKB.

Stats

• citations4
• S2 citations—
• github stars0
• HF likes0
• heat score5.24
• arxiv keymagliani2018a

Related papers

• Voronoi-based Compact Image Descriptors: Efficient Region-of-interest Retrieval With VLAD And Deep-learning-based Descriptors (2016)10.85
• A Fast Content-based Image Retrieval Method Using Deep Visual Features (2019)6.77
• Aggregated Deep Local Features For Remote Sensing Image Retrieval (2019)14.11
• Multires-netvlad: Augmenting Place Recognition Training With Low-resolution Imagery (2022)16.01
• Local Feature Detectors, Descriptors, And Image Representations: A Survey (2016)0.00
• Co-occurrence Of Deep Convolutional Features For Image Search (2020)9.76
• Image Retrieval Using Multi-scale CNN Features Pooling (2020)9.23
• Pointnetvlad: Deep Point Cloud Based Retrieval For Large-scale Place Recognition (2018)25.45