Image Search | BestHub

System Architect Go

Jul 4, 2024 · Artificial Intelligence

Optimizing Image Search System Architecture with Client‑Side Feature Extraction Using MobileNet

This article explains the architecture of an image‑search system that extracts feature vectors, stores them in a vector database, and performs similarity queries, then proposes an optimized design that offloads feature extraction to a lightweight MobileNet model running in the browser, reducing latency, server load, and component complexity.

Image SearchMobileNetSystem Architecture

0 likes · 9 min read

Optimizing Image Search System Architecture with Client‑Side Feature Extraction Using MobileNet

php中文网 Courses

Aug 30, 2023 · Artificial Intelligence

Microsoft Announces Third-Generation Turing Bletchley Visual Language Model

Microsoft unveiled its third‑generation Turing Bletchley visual language model, set to be integrated into Bing and other products to dramatically enhance image search, while also being used for content moderation on Xbox services, reflecting extensive user‑driven refinements.

AIBingImage Search

0 likes · 2 min read

Microsoft Announces Third-Generation Turing Bletchley Visual Language Model

Rare Earth Juejin Tech Community

Jul 27, 2023 · Artificial Intelligence

Implementing Text‑Based Image Search Using OCR, Transformers, and Vector Databases

This article explains how to build a text‑to‑image search system by first extracting text with OCR, then storing image paths and textual embeddings in a SQLite or Milvus vector database, and finally improving retrieval with Transformer‑based sentence embeddings and image‑captioning models.

Image SearchMilvusOCR

0 likes · 16 min read

Implementing Text‑Based Image Search Using OCR, Transformers, and Vector Databases

Baidu Geek Talk

Mar 23, 2023 · Artificial Intelligence

Advanced Image Search in Baidu Netdisk: Semantic Vector Retrieval and Multi-Modal Fusion

Baidu Netdisk’s new image search combines ERNIE‑ViL‑based semantic vectors, cross‑modal matching and metadata such as timestamps, GPS and facial tags, using LSH‑optimized indexing to let users find specific photos among billions with natural‑language queries, delivering faster, more accurate results without manual tagging.

ERNIE-ViLImage SearchLSH hashing

0 likes · 11 min read

Advanced Image Search in Baidu Netdisk: Semantic Vector Retrieval and Multi-Modal Fusion

DeWu Technology

Nov 18, 2020 · Artificial Intelligence

Evolution and Technical Analysis of Dewu Photo Search

Dewu Photo Search evolved from a limited Aliyun‑based prototype to a self‑developed pipeline using EfficientNet detection and 128‑dim embeddings, boosting top‑1 shoe accuracy over 100 % and overall precision by up to 41 %, while reducing latency and improving scalability despite remaining stability challenges.

Feature ExtractionImage Searchcomputer vision

0 likes · 10 min read

Evolution and Technical Analysis of Dewu Photo Search

iQIYI Technical Product Team

Jun 19, 2020 · Artificial Intelligence

Emoji Search at iQIYI Douya: From ElasticSearch to Lucene and Semantic Retrieval

iQIYI Douya’s emoji search evolved from ElasticSearch to a pure Lucene implementation and added semantic vector retrieval, enabling fast, scalable, and more accurate text‑based search of AI‑generated images for small‑to‑medium businesses by combining custom tokenization, dense embeddings, and hybrid ranking.

ElasticsearchImage SearchLucene

0 likes · 14 min read

Emoji Search at iQIYI Douya: From ElasticSearch to Lucene and Semantic Retrieval

System Architect Go

Jun 4, 2020 · Artificial Intelligence

Evolution and Underlying Principles of the Billion‑Scale Image Search System at Youpai Image Manager

This article describes the two‑generation evolution of Youpai Image Manager's billion‑scale image search system, explaining the mathematical representation of images, the limitations of MD5, the first‑generation pHash‑ElasticSearch solution, and the second‑generation CNN‑Milvus approach for robust, large‑scale visual similarity search.

CNNElasticsearchImage Search

0 likes · 9 min read

Evolution and Underlying Principles of the Billion‑Scale Image Search System at Youpai Image Manager

System Architect Go

Mar 30, 2020 · Artificial Intelligence

Overview of Image Search System

This article explains the fundamentals of building an image‑by‑image search system, covering image feature extraction methods such as hashing, traditional descriptors, CNN‑based vectors, and the use of vector search engines like Milvus for similarity retrieval.

CNNFeature ExtractionImage Search

0 likes · 6 min read

DataFunTalk

May 14, 2019 · Artificial Intelligence

A Comprehensive Overview of Image Search Technology: Frameworks, Evolution, and System Architecture

This article provides a thorough introduction to image‑search technology, covering its general framework, offline and online components, feature‑extraction evolution, retrieval engine structures, and architectural challenges such as dynamic indexing, feature synchronization, and high‑throughput low‑latency serving.

Feature ExtractionImage Searchcomputer vision

0 likes · 12 min read

A Comprehensive Overview of Image Search Technology: Frameworks, Evolution, and System Architecture

Youku Technology

May 6, 2019 · Artificial Intelligence

Exploring Intelligent Production at Youku: AI‑Driven Video Analysis and Automation

The talk describes Youku’s intelligent production platform, which uses AI and cloud computing to automatically analyze video frames, extract fine‑grained metadata such as scenes, persons, actions and scores, and then generate highlights, vertical clips, annotations and feedback for editors and upstream producers, while addressing challenges like pose‑tracking, graph‑based action classification and future plans for deeper video understanding and open competitions.

AIImage SearchPose Estimation

0 likes · 14 min read

Exploring Intelligent Production at Youku: AI‑Driven Video Analysis and Automation

Tongcheng Travel Technology Center

Sep 8, 2017 · Artificial Intelligence

Challenges and Techniques in Image Search: Facenet Model and Triplet Loss

The article discusses the evolution of image search engines, outlines key challenges such as image quality, watermarks, speed, and feature extraction, and explains how the Facenet deep‑learning model with Triplet loss can be used to generate compact image embeddings for efficient similarity search.

Feature ExtractionImage Searchcomputer vision

0 likes · 7 min read

Challenges and Techniques in Image Search: Facenet Model and Triplet Loss

Qunar Tech Salon

Feb 20, 2016 · Artificial Intelligence

Mobile Image Search: Algorithm Framework and Implementation at Paizhi Tao

Mobile image search has become a critical user demand, and since its 2014 launch, Alibaba’s Paizhi Tao has evolved through multiple iterations to a robust AI-driven pipeline comprising category prediction, object detection, deep and local image feature extraction, scalable retrieval indexing, and relevance-based ranking.

Image Searchdeep learningmobile AI

0 likes · 6 min read