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A Volumetric Saliency Guided image summarization for RGB-D Indoor Scene Classification

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第11期34卷 10917-10929页

作者： Meena, Preeti Kumar, Himanshu Yadav, Sandeep Kumar Indian Inst Technol Jodhpur Discipline Elect Engn Jodhpur 342037 Rajasthan India

image summary, an abridged version of the original visual content, can be used to represent the scene. Thus, tasks such as scene classification, identification, indexing, etc., can be performed efficiently using the unique summary. Saliency is the most commonly used technique for generating the relevant image summary. However, the definition of saliency is subjective in nature and depends upon the application. Existing saliency detection methods using RGB-D data mainly focus on color, texture, and depth features. Consequently, the generated summary contains either foreground objects or non-stationary objects. However, applications such as scene identification require stationary characteristics of the scene, unlike state-of-the-art methods. This paper proposes a novel volumetric saliency-guided framework for indoor scene classification. The results highlight the efficacy of the proposed method.

关键词： image summarization RGB-D image volumetric saliency saliency

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Hybrid image summarization 11

Hybrid image summarization

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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Xu, Hao Wang, Jingdong Hua, Xian-Sheng Li, Shipeng University of Science and Technology China Microsoft Research Asia China

ISBN: (纸本)9781450306164

In this paper, we address a problem of managing tagged images with hybrid summarization. We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images. We propose a novel approach, called Homogeneous and Heterogeneous Message Propagation (H2MP), which extends affinity propagation that only works over homogeneous relations to heterogeneous relations. The summary obtained by our approach is both visually and semantically satisfactory. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Copyright 2011 ACM.

关键词： image summarization affinity propagation image presentation homogeneous and heterogeneous message propagation

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Implicit Diversity in image summarization

Implicit Diversity in Image Summarization

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Proceedings of the ACM on Human-Computer Interaction

作者： Celis, L. Elisa Keswani, Vijay Yale University New HavenCT06520 United States

Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes such as gender or race. This skew goes beyond that which already exists in the world-i.e., the search results for images of people are more imbalanced than the ground truth would suggest. For example, Kay et al. showed that although 28% of CEOs in the U.S. are women, only 10% of the top 100 results for "CEO"in Google image Search are women. Similar observations abound across search terms and across socially salient attributes. Most existing approaches to correct for this kind of bias assume that the images of people include labels denoting the relevant socially salient attributes. These labels are explicitly used to either change the dataset, adjust the training of the algorithm, and/or in the execution of the method. However, such labels are often unknown. Further, using machine learning techniques to infer these labels may often not be possible within acceptable accuracy ranges and may not be desirable due to the additional biases this process could incur. As observed in prior work, alternate approaches consider the diversity of image features, which often do not translate to images of visibly diverse people. We develop a novel approach that takes as input a visibly diverse control set of images of people and uses this set as part of a procedure to select a set of images of people in response to a query. The goal is to have a resulting set that is more visibly diverse in a manner that emulates the diversity depicted in the control set. It accomplishes this by evaluating the similarity of the images selected by a black-box algorithm with the images in the diversity control set, and incorporating this "diversity score"into the final selection process. Importantly, this approach does not require images to be labelled at any point; effectively, it gives a way to implicitly diversify the set of images selected. We provide two var

关键词： image enhancement Learning systems Search engines Alternate approaches Black box algorithms Diversity control Facial feature Gender diversity image summarization Machine learning techniques State of the art algorithms

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Disaster image Filtering and summarization Based on Multi-layered Affinity Propagation

Disaster Image Filtering and Summarization Based on Multi-la...

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14th IEEE International Symposium on Multimedia (ISM)

作者： Yang, Yimin Chen, Shu-Ching Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA

ISBN: (纸本)9780769548753

In this paper, a disaster image filtering and summarization (DIFS) framework is proposed based on multilayered affinity propagation. The proposed framework is able to automatically identify and summarize latent semantic themes (scenes) in a disaster topic and filter junk images at the same time. Specifically, the images belonging to a disaster topic are first clustered into different groups based on visual descriptors using affinity propagation (AP). Then the typical instances within each cluster are collected to perform the second-layer clustering for identifying final positive clusters by utilizing both visual and textual similarities concurrently. At both layers, the proposed curve fitting function is applied to select appropriate preference values for the AP algorithm. The experimental results on the real world Flickr data set demonstrate the effectiveness of the proposed framework.

关键词： disaster topics image filtering image summarization multi-layered affinity propagation

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Social media based event summarization by user-text-image co-clustering

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KNOWLEDGE-BASED SYSTEMS 2019年 164卷 107-121页

作者： Qian, Xueming Li, Mingdi Ren, Yayun Jiang, Shuhui Xi An Jiao Tong Univ Sch Elect & Informat Engn Dept Informat & Commun Engn Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Key Lab Intelligent Networks & Network Secur Minist Educ Xian Shaanxi Peoples R China

Microblogging services have changed the way that people exchange information. There will generate a large number of data on the web once popular events or emergencies occur, including textual descriptions about the time, location and details for the event. Meanwhile users can review, comment, spread the event conveniently. It has always been a hot issue that how to use this mass of data to detect and predict breaking events. While existing approaches mostly only focus on event detection, event location estimation and text-based summary, a small amount of works have focused on event summarization. In this paper, we put forward a new social media based event summarization framework, which comprises of three stages: (1) A coarse-to-fine filtering model is exploited to eliminate irrelevant information. (2) A novel User-Text-image Co-clustering (UTICC) is proposed to jointly discover subevents from microblogs of multiple media types user, text, and image. (3)A multimedia event summarization process is designed to identify both representative texts and images, which are further aggregated to form a holistic visualized summary for the events. We conduct extensive experiments on Weibo dataset to demonstrate the superiority of the proposed framework compared to the state-of-the-art approaches. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Event summarization Social media image summarization Co-clustering

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Using Regularity Unit As Guidance For summarization-Based image Resizing

Using Regularity Unit As Guidance For Summarization-Based Im...

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IEEE International Conference on Visual Communications and image Processing (VCIP) - Visual Communications in the Era of AI and Limited Resources

作者： Hsiao, Fang-Tsung Lin, Yi-Hsien Lu, Yi-Chang Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei 10617 Taiwan

ISBN: (纸本)9781728185514

In this paper, we propose a novel algorithm for summarization-based image resizing. In the past, a process of detecting precise locations of repeating patterns is required before the pattern removal step in resizing. However, it is difficult to find repeating patterns which are illuminated under different lighting conditions and viewed from different perspectives. To solve the problem, we first identify the regularity unit of repeating patterns by statistics. Then we can use the regularity unit for shift-map optimization to obtain a better resized image. The experimental results show that our method is competitive with other well-known methods.

关键词： image resizing image summarization repeating pattern removal

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Visualization Applications and Design Studies

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2012年第1期32卷 20-21页

作者： Di Battista, Giuseppe Fekete, Jean-Daniel Qu, Huamin Third Univ Romes Dept Comp Sci & Automat Rome Italy INRIA Sophia Antipolis France Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China

This article introduces the articles in the special issue on visualization applications and design studies. The articles cover both traditional application domains such as visualization of scientific simulations and emerging ones such as visualization of text streams, image collections, trajectories, and political phenomena. [ABSTRACT FROM PUBLISHER]

关键词： agent-based simulation combustion simulation computer graphics density maps Design methodology image collections image summarization Information analysis Interactive systems Particle measurements Real time systems Simulations Special issues and sections Streaming media streaming text Streamit visual analytics visualization

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A Picture Paints a Thousand Words but Can it Paint Just One? 16

A Picture Paints a Thousand Words but Can it Paint Just One?

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11th ACM SIGCHI Conference on Designing Interactive Systems (DIS)

作者： Robb, David A. Padilla, Stefano Methven, Thomas S. Kalkreuter, Britta Chantler, Mike J. Heriot Watt Univ Sch Math & Comp Sci Edinburgh EH14 4AS Midlothian Scotland Heriot Watt Univ Sch Text & Design Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9781450340311

imagery and language are often seen as serving different aspects of cognition, with cognitive styles theories proposing that people can be visual or verbal thinkers. Most feedback systems, however, only cater to verbal thinkers. To help rectify this, we have developed a novel method of crowd communication which appeals to those more visual people. Designers can ask a crowd to feedback on their designs using specially constructed image banks to discover the perceptual and emotional theme perceived by possible future customers. A major component of the method is a summarization process in which the crowd's feedback, consisting of a mass of images, is presented to the designer as a digest of representative images. In this paper we describe an experiment showing that these image summaries are as effective as the full image selections at communicating terms. This means that designers can consume the new feedback confident that it represents a fair representation of the total image feedback from the crowd.

关键词： Design feedback image browsing similarity clustering semiotics image summarization visual communication perception emotion mood crowdsourcing

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image-based Emotion Feedback: How Does the Crowd Feel? And Why? 17

Image-based Emotion Feedback: How Does the Crowd Feel? And W...

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12th ACM SIGCHI Designing Interactive Systems (DIS) Conference

ISBN: (纸本)9781450349222

In previous work we developed a method for interior designers to receive image-based feedback about a crowd's emotions when viewing their designs. Although the designers clearly desired a service which provided the new style of feedback, we wanted to find out if an internet crowd would enjoy, and become engaged in, giving emotion feedback this way. In this paper, through a mixed methods study, we expose whether and why internet users enjoy giving emotion feedback using images compared to responding with text. We measured the participants' cognitive styles and found that they correlate with the reported utility and engagement of using images. Those more visual than they are verbal were more engaged by using images to express emotion compared to text. Enlightening qualitative insights reveal, surprisingly, that half of our participants have an appetite for expressing emotions this way, value engagement over clarity, and would use images for emotion feedback in contexts other than design feedback.

关键词： Cognitive styles affective computing creativity design feedback crowdsourcing perceptual and emotional feedback image summarization

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Smart photo sticking

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5th International Workshop on Adaptive Multimedia Retrieval

作者： Battiato, Sebastiano Ciocca, Gianluigi Gasparini, Francesca Puglisi, Giovanni Schettini, Raimondo Univ Catania Dipartimento Matemat & Informat Viale A Doria 6 I-95125 Catania Italy Univ Milano Bicocca Dept Info Sistemistica e Comuni I-20126 Milan Italy

ISBN: (纸本)9783540798590

Smart photo sticking is a novel strategy to automatically arrange a collection of photos in a pleasant collage. The proposed approach improves previous solutions both considering a self-adaptive image cropping algorithm, exploiting visual and semantic information, and introducing an optimization process based on a genetic algorithm. Preliminary results confirm the effectiveness of the proposed strategy on a heterogeneous collection of non professional photos.

关键词： image summarization saliency region genetic algorithm

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