BestHub
Sign in
Tagged articles
32 articles
Page 1 of 1
DataFunSummit
DataFunSummit
Feb 26, 2025 · Artificial Intelligence

Applying Multimodal Large Models to Music Recommendation at NetEase Cloud Music

This article details how NetEase Cloud Music leverages multimodal large language models to improve music recommendation across daily, personalized, and playlist scenarios by extracting rich audio, text, and visual features, addressing data skew, cold‑start challenges, and achieving measurable gains in user engagement and distribution efficiency.

Multimodal AINetEase Cloud Musicfeature extraction
0 likes · 12 min read
Applying Multimodal Large Models to Music Recommendation at NetEase Cloud Music
php Courses
php Courses
Oct 23, 2024 · Artificial Intelligence

Data Dimensionality Reduction and Feature Extraction with PHP

This article explains the concepts of data dimensionality reduction and feature extraction in machine learning and demonstrates how to implement them in PHP using the PHP‑ML library, including installation, data preprocessing, PCA-based reduction, and feature extraction with token vectorization and TF‑IDF.

PCAPHP-MLdimensionality reduction
0 likes · 5 min read
Data Dimensionality Reduction and Feature Extraction with PHP
php Courses
php Courses
Jun 13, 2024 · Artificial Intelligence

Using PHP for Data Dimensionality Reduction and Feature Extraction

This article explains the importance of data dimensionality reduction and feature extraction in machine learning, and provides a step‑by‑step guide with PHP code examples—including library installation, data preprocessing, PCA‑based reduction, and feature selection techniques—demonstrating how to handle large datasets efficiently.

PCAPHPdata preprocessing
0 likes · 6 min read
Using PHP for Data Dimensionality Reduction and Feature Extraction
TAL Education Technology
TAL Education Technology
Aug 31, 2023 · Artificial Intelligence

Research on Content-Based Image Retrieval Techniques

This article reviews the fundamentals, feature extraction methods, evaluation metrics, and common datasets of content‑based image retrieval (CBIR), discussing traditional low‑level features, local descriptors, unsupervised and supervised learning approaches, and recent deep‑learning models for improving retrieval performance.

CBIRDatasetsDeep Learning
0 likes · 13 min read
Research on Content-Based Image Retrieval Techniques
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 25, 2023 · Artificial Intelligence

Building a Reverse Image Search Engine with Geometric Distance, ResNet Feature Embeddings, Clustering, and Milvus Vector Database

This article walks through implementing a reverse image search system, starting with simple pixel‑based geometric distance, then improving accuracy using ResNet‑derived feature embeddings, accelerating queries with K‑means clustering, and finally deploying a Milvus vector database for fast, scalable similarity retrieval.

MilvusResNet50clustering
0 likes · 17 min read
Building a Reverse Image Search Engine with Geometric Distance, ResNet Feature Embeddings, Clustering, and Milvus Vector Database
Programmer DD
Programmer DD
Jun 25, 2023 · Artificial Intelligence

How to Build Image Search with Elasticsearch 8.x and CLIP Multilingual Model

This article explains the concept of image‑based search, why it matters, and provides a step‑by‑step guide to implement image search using Elasticsearch 8.x, feature‑extraction libraries, and the multilingual CLIP‑ViT‑B‑32 model, including code snippets and architecture overview.

Deep Learningclip modelfeature extraction
0 likes · 10 min read
How to Build Image Search with Elasticsearch 8.x and CLIP Multilingual Model
High Availability Architecture
High Availability Architecture
Apr 27, 2023 · Artificial Intelligence

Design and Optimization of Bilibili's Large‑Scale Video Duplicate Detection System

This article describes the design, algorithmic improvements, and engineering performance optimizations of Bilibili's massive video duplicate detection (collision) system, covering challenges of low‑edit‑degree reposts, two‑stage retrieval, self‑supervised feature extraction, GPU‑accelerated preprocessing, and the resulting gains in accuracy and throughput.

BilibiliDeep Learningfeature extraction
0 likes · 17 min read
Design and Optimization of Bilibili's Large‑Scale Video Duplicate Detection System
Bilibili Tech
Bilibili Tech
Apr 21, 2023 · Artificial Intelligence

Design and Optimization of Bilibili's Large-Scale Video Duplicate Detection System

Bilibili built a massive video‑duplicate detection platform that trains a self‑supervised ResNet‑50 feature extractor, removes black borders, and uses a two‑stage ANN‑plus‑segment‑level matching pipeline accelerated by custom GPU decoding and inference, boosting duplicate rejection 7.5×, recall 3.75×, and cutting manual misses from 65 to 5 per day.

Deep LearningGPU Accelerationfeature extraction
0 likes · 19 min read
Design and Optimization of Bilibili's Large-Scale Video Duplicate Detection System
DataFunSummit
DataFunSummit
Mar 12, 2023 · Artificial Intelligence

PaddleBox and FeaBox: GPU‑Based Large‑Scale Sparse Model Training and Integrated Feature Extraction Frameworks at Baidu

The article introduces PaddleBox and FeaBox, two GPU‑driven frameworks designed for massive sparse DNN training and unified feature extraction, detailing their architecture, performance advantages, hardware‑software co‑design challenges, and successful deployment across Baidu's advertising systems.

FeaBoxGPUPaddleBox
0 likes · 24 min read
PaddleBox and FeaBox: GPU‑Based Large‑Scale Sparse Model Training and Integrated Feature Extraction Frameworks at Baidu
Tencent Advertising Technology
Tencent Advertising Technology
Jun 22, 2021 · Artificial Intelligence

Technical Insights and Solution Strategies from the Tencent Advertising Algorithm Competition – Video Ad Track

The article outlines the Tencent Advertising Algorithm Competition’s video ad challenge, details the paper submission guidelines, and shares a participant’s step‑by‑step technical approach—including baseline experiments, model re‑implementation with Paddle, multimodal feature extraction, optimizer choices, and future improvement directions—providing practical AI insights for multimedia video classification.

Deep LearningMultimodal LearningTencent competition
0 likes · 7 min read
Technical Insights and Solution Strategies from the Tencent Advertising Algorithm Competition – Video Ad Track
MaGe Linux Operations
MaGe Linux Operations
Feb 13, 2021 · Fundamentals

Extracting Signal Features with Hilbert Transform and Bispectrum in Python

This article explains how to use the Hilbert transform to obtain signal feature values, defines R and J envelope statistics and bispectrum features, and provides Python code that generates signals, adds noise, computes these features, and visualizes their variation with amplitude, frequency, and phase.

Hilbert transformPythonSignal Processing
0 likes · 13 min read
Extracting Signal Features with Hilbert Transform and Bispectrum in Python
360 Quality & Efficiency
360 Quality & Efficiency
Nov 27, 2020 · Artificial Intelligence

Image Similarity Detection Methods: Hashing, Histograms, Feature Matching, BOW+K‑Means, and CNN‑Based Approaches

This article reviews common image similarity detection techniques—including hash-based methods (aHash, pHash, dHash), histogram comparison, feature matching with ORB and SIFT/SURF, bag‑of‑words with K‑Means, and CNN‑based VGG16 features—detailing their algorithms, Python implementations, performance characteristics, and practical considerations.

Computer VisionDeep LearningHashing
0 likes · 15 min read
Image Similarity Detection Methods: Hashing, Histograms, Feature Matching, BOW+K‑Means, and CNN‑Based Approaches
DeWu Technology
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.

Deep LearningModel Optimizationfeature extraction
0 likes · 10 min read
Evolution and Technical Analysis of Dewu Photo Search
System Architect Go
System Architect Go
Apr 11, 2020 · Artificial Intelligence

How to Build an Image Search Engine with CNN and Milvus: A Step‑by‑Step Guide

This article walks through the complete engineering workflow for building an image‑search system, covering CNN‑based feature extraction with VGG16, vector normalization, image preprocessing, black‑edge removal, and practical deployment of the Milvus vector database including hardware requirements, capacity planning, collection/partition design, and search result handling.

CNNMilvusPython
0 likes · 11 min read
How to Build an Image Search Engine with CNN and Milvus: A Step‑by‑Step Guide
System Architect Go
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.

CNNMilvusfeature extraction
0 likes · 6 min read
Overview of Image Search System
iQIYI Technical Product Team
iQIYI Technical Product Team
Mar 27, 2020 · Artificial Intelligence

Beihang Team's Video Copyright Detection Solution: Frame Sampling, Feature Extraction, and Retrieval Matching

The Beihang University team’s video copyright detection solution samples frames every 200 ms, extracts 512‑dimensional ResNet‑18 features, and uses handcrafted cosine‑similarity matching to identify source videos and plagiarized segments, all while operating on limited hardware without training any models.

algorithm designfeature extractionframe sampling
0 likes · 12 min read
Beihang Team's Video Copyright Detection Solution: Frame Sampling, Feature Extraction, and Retrieval Matching
iQIYI Technical Product Team
iQIYI Technical Product Team
Mar 13, 2020 · Artificial Intelligence

How to Detect Video Copyright Infringement with Two‑Stage Frame Matching

This article details a two‑stage video copyright detection pipeline that builds a frame‑level feature library, uses Hessian‑Affine + SIFT and Fisher Vectors for robust feature extraction, applies weighted bipartite graph matching and longest increasing subsequence localization, and achieves an F1‑score of 0.9086 on the CCF 2019 competition dataset.

AIfeature extractionframe matching
0 likes · 14 min read
How to Detect Video Copyright Infringement with Two‑Stage Frame Matching
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 3, 2020 · Industry Insights

How iQIYI Boosted Click‑Through Rates with AI‑Powered Personalized Poster Generation

This article examines iQIYI's end‑to‑end personalized poster production and distribution system, detailing AI‑driven image cropping, smart frame extraction, feature extraction, multi‑armed bandit ranking, and online experiments that together significantly increased poster click‑through rates on TV and mobile platforms.

AI poster generationVideo platformfeature extraction
0 likes · 12 min read
How iQIYI Boosted Click‑Through Rates with AI‑Powered Personalized Poster Generation
DataFunTalk
DataFunTalk
Nov 28, 2019 · Artificial Intelligence

Web Data Mining and Page Analysis Techniques for Search Engines

This article explains how search engines collect, analyze, and rank web pages by describing the spider system, HTML and layout tree construction, feature extraction, and machine‑learning based classification methods used to understand page content and improve result relevance.

HTML treefeature extractionlayout tree
0 likes · 8 min read
Web Data Mining and Page Analysis Techniques for Search Engines
58 Tech
58 Tech
Oct 10, 2019 · Big Data

Optimizing Real‑Time Feature Extraction at 58.com: Migrating from Spark Streaming to Flink

This article describes how 58.com’s commercial engineering team redesigned its real‑time feature‑mining pipeline—replacing a minute‑level Spark Streaming framework with Flink—to achieve sub‑second latency, higher throughput, stronger fault‑tolerance, and end‑to‑end exactly‑once semantics for user‑profile generation in the second‑hand‑car recommendation scenario.

Big DataExactly-OnceFlink
0 likes · 14 min read
Optimizing Real‑Time Feature Extraction at 58.com: Migrating from Spark Streaming to Flink
DataFunTalk
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.

Computer Visionfeature extractionimage search
0 likes · 12 min read
A Comprehensive Overview of Image Search Technology: Frameworks, Evolution, and System Architecture
Tencent Cloud Developer
Tencent Cloud Developer
Apr 16, 2019 · Artificial Intelligence

Building Image Recognition Systems: From Basics to Advanced AI Techniques

This article summarizes a computer‑vision salon where Dr. Ji Yongnan explains imaging pipelines, traditional feature‑based methods, deep‑learning breakthroughs, Tencent Cloud AI services, real‑world case studies, and answers audience questions about machine‑vision versus computer‑vision and data‑scarcity challenges.

AI applicationsComputer VisionDeep Learning
0 likes · 18 min read
Building Image Recognition Systems: From Basics to Advanced AI Techniques
System Architect Go
System Architect Go
Mar 14, 2019 · Artificial Intelligence

Understanding Image Similarity: Image Hashing and Feature-Based Methods

This article explains why simple MD5 checks cannot assess image similarity and introduces two major approaches—image hashing and image feature extraction—detailing their algorithms, practical performance, and how to compare images efficiently using Hamming distance and indexing techniques.

Computer VisionHamming distancefeature extraction
0 likes · 7 min read
Understanding Image Similarity: Image Hashing and Feature-Based Methods
JD Tech Talk
JD Tech Talk
Jan 16, 2019 · Artificial Intelligence

Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results

This article presents a detailed case study of building a purchase‑user prediction model by integrating Convolutional Neural Networks for feature extraction with Long Short‑Term Memory networks for time‑series forecasting, covering background, model structure, data augmentation, experimental results, and business impact.

CNNDeep LearningLSTM
0 likes · 10 min read
Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results
360 Quality & Efficiency
360 Quality & Efficiency
Dec 7, 2018 · Artificial Intelligence

Image Feature Extraction and Clustering for Key Frame Selection in Mobile App Installation Screenshots

This article presents a technical solution for extracting representative key frames from time‑series screenshots of a mobile app installation process, covering pixel sampling, dimensionality reduction, classic feature extractors (SIFT, HOG, ORB), auto‑encoder based deep learning, and clustering methods such as KMeans and DBSCAN, along with practical results and performance analysis.

AutoencoderComputer VisionHOG
0 likes · 5 min read
Image Feature Extraction and Clustering for Key Frame Selection in Mobile App Installation Screenshots
Meitu Technology
Meitu Technology
Jul 12, 2018 · Artificial Intelligence

DeepHash: Large-Scale Multimedia Content Analysis and Retrieval for Short Video Platforms

DeepHash is Meitu’s large‑scale short‑video analysis and retrieval system that converts deep‑learned visual features into compact binary hash codes via a MobileNet‑based CNN and triplet‑loss training, enabling fast, robust similarity search across billions of videos with sub‑second latency and minimal storage.

feature extractionlarge scalemultimedia retrieval
0 likes · 15 min read
DeepHash: Large-Scale Multimedia Content Analysis and Retrieval for Short Video Platforms
Xianyu Technology
Xianyu Technology
Apr 26, 2018 · Artificial Intelligence

Client‑Side Image Similarity Computation: Methods, Experiments, and Findings

This study compares hash‑based, CNN‑based, and local‑feature methods for client‑side image similarity detection in e‑commerce, showing that while hash methods are fast and CNNs are accurate but costly, the Hessian‑Affine detector combined with SIFT descriptors delivers the optimal balance of computational efficiency, robustness to transformations, and high recall/precision for on‑device duplicate filtering.

CNNMobile ComputingSIFT
0 likes · 11 min read
Client‑Side Image Similarity Computation: Methods, Experiments, and Findings
Tongcheng Travel Technology Center
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.

Computer VisionDeep Learningfacenet
0 likes · 7 min read
Challenges and Techniques in Image Search: Facenet Model and Triplet Loss