Web Scale Photo Hash Clustering on A Single Machine
This paper addresses the problem of clustering a very large number of photos (i.e. hundreds of millions a day) in a stream into millions of clusters. This is particularly important as the popularity of photo sharing websites, such as Facebook, Google, and Instagram. Given large number of photos available online, how to efficiently organize them is an open problem. To address this problem, we propose to cluster the binary hash codes of a large number of photos into binary cluster centers. We present a fast binary k-means algorithm that works directly on the similarity-preserving hashes of images and clusters them into binary centers on which we can build hash indexes to speedup computation. The proposed method is capable of clustering millions of photos on a single machine in a few minutes. We show that this approach is usually several magnitude faster than standard k-means and produces comparable clustering accuracy. In addition, we propose an online clustering method based on binary k-means that is capable of clustering large photo stream on a single machine, and show applications to spam detection and trending photo discovery.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringOnline ClusteringSpam detectionSimilar Papers 제목 키워드 기반
Web-Scale Image Clustering Revisited
Large scale duplicate detection, clustering and mining of documents or images has been conventionally treated with seed detection via hashing, followed by seed growing heuristics using fast search. Principled clustering …
ClusteringImage ClusteringQuantizationLOH and behold: Web-scale visual search, recommendation and clustering using Locally Optimized Hashing
We propose a novel hashing-based matching scheme, called Locally Optimized Hashing (LOH), based on a state-of-the-art quantization algorithm that can be used for efficient, large-scale search, recommendation, clustering,…
ClusteringDistributed ComputingQuantizationTPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering
In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among…
ClusteringCPUGraph-based Multi-view Binary Learning for Image Clustering
Hashing techniques, also known as binary code learning, have recently gained increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering methods are single-view ones, which lac…
ClusteringGraph EmbeddingImage ClusteringHARRISON: A Benchmark on HAshtag Recommendation for Real-world Images in Social Networks
Simple, short, and compact hashtags cover a wide range of information on social networks. Although many works in the field of natural language processing (NLP) have demonstrated the importance of hashtag recommendation, …