Measuring and Predicting Object Importance

Spain, Merrielle; Perona, Pietro

doi:10.1007/s11263-010-0376-0

Cited by 68 publications

(69 citation statements)

References 28 publications

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“…Elazary and Itti (2008) propose learning to predict object interestingness by the order in which objects are labelled by annotators in LabelMe. Spain and Perona (2010) propose learning to predict object importance, by asking multiple annotators (25 per image) to name 10 objects they see in each image. The annotations are then aggregated: important objects are those that are mentioned by many annotators.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 9th International Natural Language Generation conference

Kok

2016

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Section: Related Workmentioning

confidence: 99%

Proceedings of the 9th International Natural Language Generation conference

Kok

2016

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Section: Visual Importance and Depth Evaluationmentioning

confidence: 99%

“…As explained in [39], to measure the importance of an object in a photograph of a complex scene, object position and size are particularly informative whereas some popular saliency measures are not. Moreover, using many features is not usually necessary in predicting the object importance [39]. In our algorithm, we first use the Gaussian Mixture Model (GMM) based method [9] to estimate the layering relation among the object instances and calculate the percent of each object overlapped by other outlined objects (denoted as p ol ).…”

Section: Visual Importance and Depth Evaluationmentioning

confidence: 99%

Summarization-Based Image Resizing by Intelligent Object Carving

Dong

Zhou

Lee

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Abstract-Image resizing can be more effectively achieved with a better understanding of image semantics. In this paper, similar patterns that exist in many real-world images are analyzed. By interactively detecting similar objects in an image, the image content can be summarized rather than simply distorted or cropped. This method enables the manipulation of image pixels or patches as well as semantic objects in the scene during image resizing process. Given the special nature of similar objects in a general image, the integration of a novel object carving (OC) operator with the multi-operator framework is proposed for summarizing similar objects. The object removal sequence in the summarization strategy directly affects resizing quality. The method by which to evaluate the visual importance of the object as well as to optimally select the candidates for object carving is demonstrated. To achieve practical resizing applications for general images, a template matching-based method is developed. This method can detect similar objects even when they are of various colors, transformed in terms of perspective, or partially occluded. To validate the proposed method, comparisons with state-of-the-art resizing techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.

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“…Attending to this maxim will become increasingly important as the quality and coverage of object, attribute, and scene detectors increases. It would be undesirable to develop models that describe every detected object in an image because that would be likely to violate the maxim of Quantity (Spain and Perona, 2010). Similarly, if it is possible to associate an accurate attribute with each object in the image, it will be important to be sparing in the application of those attributes: is it relevant to describe "furry" sheep when there are no sheared sheep in an image?…”

Section: A Poodle or A Dog? Evaluating Automatic Image Annotation Usingmentioning

confidence: 99%

Proceedings of the Third Workshop on Vision and Language

2014

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PrefaceThe Workshop on Vision and Language 2014 (VL'14) took place in Dublin on 23rd July 2014, as part of COLING'14. It was the joint 3rd meeting of the EPSRC Network On Vision and Language and 1st technical meeting of the new European Network on Integrating Vision and Language which is funded as a European COST Action. The VL workshops have the general aims:1. to provide a forum for reporting and discussing planned, ongoing and completed research that involves both language and vision; and 2. to enable NLP and computer vision researchers to meet, exchange ideas, expertise and technology, and form new research partnerships.As funding for the V&L EPSRC Network (EP/H018557) ends and funding for the iV&L Net European COST Action (IC1307) starts, the focus of the VL workshops will shift onto integration and joint modelling of language and vision. iV&L Net will take over the organisation of annual VL workshops for the next four years as the flagship workshop of this new COST Action.The call for papers for VL'14 was issued in May 2014 and elicited a good number of highquality submissions, each of which was peer-reviewed by three members of the programme committee. The interest in the workshop from leading NLP and computer vision researchers and the quality of submissions was high, so we aimed to be as inclusive as possible within the practical constraints of the workshop. In the end we accepted 14 submissions as long papers, and eight as short papers.The resulting workshop programme packed a lot of exciting content into one day. We were delighted to be able to include in the programme a keynote presentation by Alex Jaimes of Yahoo! Inc., an internationally leading vision researcher. Our technical programme combined seven oral papers, seven long poster papers and seven short poster papers. Some thematic clusters emerged: combined text and image processing (Nguyen et al., Sakaki et al., Jones et al., Zhang et al., HaCohen-Kerner et al.), image description, annotation and labelling (Elliott, Liparas et al., Wang et al., Jokinen and Wilcock), data set creation (Weiland et al., Le et al., McGuinness et al.), situated dialogue (Summers-Stay et al., Schütte et al.), video analysis (Bhat and Olszewska, Shrestha et al.), aids for visually impaired people (Safi et al., Belz and Bharath), and visual analysis supported by text/speech features (Anbarjafari and Aabloo). The programme also included a discussion session on future directions for the VL community and workshops, including plans for shared task competitions.We would like to thank all the people who have contributed to the organisation and delivery of this workshop: the authors who submitted such high quality papers; the programme committee for their prompt and effective reviewing; our keynote speaker, Alex Jaimes; the COLING 2014 organising committee, especially the workshops chairs, Jennifer Foster, Dan Gildea, and Tim Baldwin; the participants in the workshop; and future readers of these proceedings for your shared interest in this exciting new area of research. Aug...

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Measuring and Predicting Object Importance

Cited by 68 publications

References 28 publications

Proceedings of the 9th International Natural Language Generation conference

Proceedings of the 9th International Natural Language Generation conference

Summarization-Based Image Resizing by Intelligent Object Carving

Proceedings of the Third Workshop on Vision and Language

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