Semantic Image Retrieval By Uniting Deep Neural Networks And Cognitive Architectures

Abstract

Image and video retrieval by their semantic content has been an important and challenging task for years, because it ultimately requires bridging the symbolic/subsymbolic gap. Recent successes in deep learning enabled detection of objects belonging to many classes greatly outperforming traditional computer vision techniques. However, deep learning solutions capable of executing retrieval queries are still not available. We propose a hybrid solution consisting of a deep neural network for object detection and a cognitive architecture for query execution. Specifically, we use YOLOv2 and OpenCog. Queries allowing the retrieval of video frames containing objects of specified classes and specified spatial arrangement are implemented.

Stats

• citations8
• S2 citations—
• github stars0
• HF likes0
• heat score7.16
• arxiv keypotapov2018semantic

Related papers

• Content-based Image Retrieval And The Semantic Gap In The Deep Learning Era (2020)7.50
• Medical Image Retrieval Using Deep Convolutional Neural Network (2017)19.35
• A Multimodal Deep Learning Framework For Scalable Content Based Visual Media Retrieval (2021)0.00
• Video Retrieval Based On Deep Convolutional Neural Network (2017)9.03
• Integrating Visual And Semantic Similarity Using Hierarchies For Image Retrieval (2023)4.52
• Deep Learning For Instance Retrieval: A Survey (2021)16.05
• Tasks Integrated Networks: Joint Detection And Retrieval For Image Search (2020)11.08
• A Decade Survey Of Content Based Image Retrieval Using Deep Learning (2020)18.33