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Streaming Non-monotone Submodular Maximization: Personalized Video Summarization on the Fly [article]

Baharan Mirzasoleiman, Stefanie Jegelka, Andreas Krause
2017 arXiv   pre-print
While efficient streaming methods have been recently developed for monotone submodular maximization, in a wide range of applications, such as video summarization, the underlying utility function is non-monotone  ...  The need for real time analysis of rapidly producing data streams (e.g., video and image streams) motivated the design of streaming algorithms that can efficiently extract and summarize useful information  ...  In general, the above submodular function is very non-monotone, and we need techniques for maximizing a non-monotone submodular function in the streaming setting.  ... 
arXiv:1706.03583v3 fatcat:j3d5wz7m6vdkfjvdzeyahgv3u4

Streaming submodular maximization

Ashwinkumar Badanidiyuru, Baharan Mirzasoleiman, Amin Karbasi, Andreas Krause
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
data size.  ...  Thus, such problems can be reduced to maximizing a submodular set function subject to a cardinality constraint. Classical approaches to submodular maximization require full access to the data set.  ...  Submodular optimization over data streams.  ... 
doi:10.1145/2623330.2623637 dblp:conf/kdd/BadanidiyuruMKK14 fatcat:ybb6hl24fzg7hjh742tyswd2nm

Stream Clipper: Scalable Submodular Maximization on Stream [article]

Tianyi Zhou, Jeff Bilmes
2018 arXiv   pre-print
We propose a streaming submodular maximization algorithm "stream clipper" that performs as well as the offline greedy algorithm on document/video summarization in practice.  ...  In news and video summarization experiments, the algorithm consistently outperforms other streaming methods, and, while using significantly less computation and memory, performs similarly to the offline  ...  Video Summarization We apply lazy greedy, sieve-streaming, and stream clipper to 25 videos from video summarization dataset SumMe [10] 2 .  ... 
arXiv:1606.00389v3 fatcat:ikbjc3w7xvf2pn34arwagu4jlm

A Unified Multi-Faceted Video Summarization System [article]

Anurag Sahoo, Vishal Kaushal, Khoshrav Doctor, Suyash Shetty, Rishabh Iyer, Ganesh Ramakrishnan
2017 arXiv   pre-print
This paper addresses automatic summarization and search in visual data comprising of videos, live streams and image collections in a unified manner.  ...  Most importantly, we also show that we can summarize hours of video data in a few seconds, and our system allows the user to generate summaries of various lengths and types interactively on the fly.  ...  Introduction Visual Data in the form of images, videos and live streams have been growing at an unprecedented rate in the last few years.  ... 
arXiv:1704.01466v1 fatcat:gajvfm2kp5cvvb6kceohzamzde

Weakly Submodular Function Maximization Using Local Submodularity Ratio [article]

Richard Santiago, Yuichi Yoshida
2020 arXiv   pre-print
We provide applications of our results for monotone and non-monotone maximization problems.  ...  In this work we introduce two natural generalizations of weak submodularity for non-monotone functions.  ...  Fast constrained submodular maximization: Personalized data summarization.  ... 
arXiv:2004.14650v2 fatcat:ym3qnirktbdnvaap34g4iq2egm

The Power of Subsampling in Submodular Maximization [article]

Christopher Harshaw, Ehsan Kazemi, Moran Feldman, Amin Karbasi
2021 arXiv   pre-print
We empirically demonstrate the effectiveness of our algorithms on video summarization, location summarization, and movie recommendation tasks.  ...  In the streaming setting, we present SampleStreaming, which obtains a (4p +2 - o(1))-approximation for maximizing a submodular function subject to a p-matchoid using O(k) memory and O(km/p) evaluation  ...  The first streaming algorithm for non-monotone submodular maximization was given by Buchbinder et al. [2015] , whose randomized algorithm achieves 11.197 approximation for non-monotone maximization under  ... 
arXiv:2104.02772v1 fatcat:jh2fgeuwuvgsdbh4gzbztemkb4

Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance [article]

Vishal Kaushal, Rishabh Iyer, Khoshrav Doctor, Anurag Sahoo, Pratik Dubal, Suraj Kothawade, Rohan Mahadev, Kunal Dargan, Ganesh Ramakrishnan
2019 arXiv   pre-print
This paper addresses automatic summarization of videos in a unified manner.  ...  in the video).  ...  Introduction Visual Data in the form of images, videos and live streams have been growing at an unprecedented rate in the last few years.  ... 
arXiv:1901.01153v1 fatcat:hungkfpajvgrfov7iwhob4djyy

Cache-Aided Interactive Multiview Video Streaming in Small Cell Wireless Networks [article]

Eirina Bourtsoulatze, Deniz Gündüz
2018 arXiv   pre-print
The emergence of novel interactive multimedia applications with high rate and low latency requirements has led to a drastic increase in the video data traffic over wireless cellular networks.  ...  We then provide an equivalent formulation based on submodular set function maximization and propose a greedy solution with 1/2(1-e^-1) approximation guarantee.  ...  Submodular function maximization with a d-dimensional knapsack constraint Let g : 2 W → R be a monotone non-decreasing submodular function defined over the ground set W.  ... 
arXiv:1804.01035v1 fatcat:qbxzpcsl5jg5zhy72vbplyerky

Streaming Algorithms for News and Scientific Literature Recommendation: Submodular Maximization with a d-Knapsack Constraint [article]

Qilian Yu, Easton Li Xu, Shuguang Cui
2016 arXiv   pre-print
In this paper, we focus on the problem of maximizing a monotone submodular function subject to a d-knapsack constraint, for which we propose a streaming algorithm that achieves a (1/1+2d-ϵ)-approximation  ...  Submodular maximization problems belong to the family of combinatorial optimization problems and enjoy wide applications.  ...  STREAMING ALGORITHMS FOR MAXIMIZING MONOTONE SUBMODULAR FUNCTIONS A.  ... 
arXiv:1603.05614v3 fatcat:3je63wrolfbfzp576nyqad7axq

Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? [article]

Lin Chen, Moran Feldman, Amin Karbasi
2017 arXiv   pre-print
To the best of our knowledge, this is the first algorithm with a non-trivial approximation guarantee for maximizing a weakly submodular function subject to a constraint other than the simple cardinality  ...  Submodular functions are a broad class of set functions, which naturally arise in diverse areas. Many algorithms have been suggested for the maximization of these functions.  ...  In particular, algorithms for maximizing a submodular function subject to various constraints have found many applications in machine learning and data mining, including data summarization [37, 45] ,  ... 
arXiv:1707.04347v1 fatcat:ilti6a5dojamba6e55fiwbdv2i

Optimal Streaming Algorithms for Submodular Maximization with Cardinality Constraints [article]

Naor Alaluf, Alina Ene, Moran Feldman, Huy L. Nguyen, Andrew Suh
2020 arXiv   pre-print
We study the problem of maximizing a non-monotone submodular function subject to a cardinality constraint in the streaming model.  ...  At the end of the stream, our algorithm post-processes its data structure using any offline algorithm for submodular maximization, and obtains a solution whose approximation guarantee is α/1+α-ε, where  ...  Streaming non-monotone submodular maximization: Personalized video summarization on the fly. In Thirty-second AAAI conference on artificial intelligence, 2018.  ... 
arXiv:1911.12959v3 fatcat:rci55cucxbgudoi4asppuqieji

Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint [article]

Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser
2020 arXiv   pre-print
Focusing on these challenges, we revisit the classic problem of maximizing a (possibly non-monotone) submodular function subject to a knapsack constraint.  ...  Constrained submodular maximization problems encompass a wide variety of applications, including personalized recommendation, team formation, and revenue maximization via viral marketing.  ...  The total potential revenue v(S) = i∈A S v i (S) that we aim to maximize is a non-monotone submodular function.  ... 
arXiv:2007.05014v1 fatcat:ynt4meyn7jfulen6c6idgc4yc4

Distributed Submodular Maximization [article]

Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause
2016 arXiv   pre-print
We begin with monotone submodular maximization subject to a cardinality constraint, and then extend this approach to obtain approximation guarantees for (not necessarily monotone) submodular maximization  ...  In this paper, we consider the problem of submodular function maximization in a distributed fashion.  ...  Table 1 summarizes the approximation guarantees for monotone and non-monotone submodular maximization under different constraints.  ... 
arXiv:1411.0541v2 fatcat:argsl5avqrdnzin64rype6xmmm

Fully Dynamic Algorithm for Constrained Submodular Optimization [article]

Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakub Tarnawski, Morteza Zadimoghaddam
2020 arXiv   pre-print
The task of maximizing a monotone submodular function under a cardinality constraint is at the core of many machine learning and data mining applications, including data summarization, sparse regression  ...  Our main result is a randomized algorithm that maintains an efficient data structure with a poly-logarithmic amortized update time and yields a (1/2-ϵ)-approximate solution.  ...  We study the problem of maximizing a non-negative monotone submodular function f : 2 V → R ≥0 .  ... 
arXiv:2006.04704v1 fatcat:enk7gqxxafg57jguhapc2svnxu

Deep Submodular Functions [article]

Jeffrey Bilmes, Wenruo Bai
2017 arXiv   pre-print
We start with an overview of a class of submodular functions called SCMMs (sums of concave composed with non-negative modular functions plus a final arbitrary modular).  ...  DSFs can be motivated by considering a hierarchy of descriptive concepts over ground elements and where one wishes to allow submodular interaction throughout this hierarchy.  ...  Suppose that h : 2 V → R is a monotone non-decreasing submodular function and φ is a monotone non-decreasing concave function. Then g(A) = φ(h(A)) is monotone non-decreasing submodular. Proof.  ... 
arXiv:1701.08939v1 fatcat:zvbpgq7d2feqrantvl7hi34jum
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