Automatic thumbnail cropping and its effectiveness

Suh, Bongwon; Ling, Haibin; Bederson, Benjamin B.; Jacobs, David W.

doi:10.1145/964696.964707

Cited by 379 publications

(262 citation statements)

References 19 publications

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“…Performance of the proposed scheme is also comparable to other schemes for images with single important object. Columns from left to right: down sampled version of the original image, output of the proposed scheme, image cropping [1] and seam curving base method [8], respectively Evaluation of retargeting is essentially of subjective nature. Hence, it is resorted to manual judgments for evaluation.…”

Section: Resultsmentioning

confidence: 99%

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Image Retargeting through Constrained Growth of Important Rectangular Partitions

Pal

Mukhopadhyay

Mitra

2011

Lecture Notes in Computer Science

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Abstract. The challenge of displaying an image in a much smaller target area is to maintain recognizability of its contents. A novel rectangular partitioning based retargeting scheme is proposed in this paper to address this issue. Constrained growth of rectangular partitions containing important objects is major characteristic of this scheme. Subjective evaluation by a group of volunteers establishes usefulness of this scheme.

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Section: Resultsmentioning

confidence: 99%

“…Content-aware processing addresses this issue by differentiating image contents on the basis of their importance. Image cropping [1,2,3] and fisheye-view warping [4] are effective for images with single important object. But they are not that much effective when important contents are sparsely distributed within the image.…”

Section: Introductionmentioning

confidence: 99%

Image Retargeting through Constrained Growth of Important Rectangular Partitions

Pal

Mukhopadhyay

Mitra

2011

Lecture Notes in Computer Science

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“…Traditionally, a thumbnail is created by shrinking from an input image. However, as demonstrated in [1], these methods are usually less effective especially for very small thumbnails that are particularly important for small screen devices such as PDA and mobile phone. For this reason, intelligent image summarization has recently attracted many research efforts.…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of thumbnail cropping is shown in Suh et al [1]. A key issue is to decide the cropping rectangle.…”

Section: Introductionmentioning

confidence: 99%

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Learning based thumbnail cropping

Ling

2009

2009 IEEE International Conference on Multimedia and Expo

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Thumbnail cropping helps improve thumbnail readability by cropping images before shrinking them. In this paper we propose a learning based method for automatic thumbnail cropping. To this end, we use a support vector machine to learn a discriminative model that simultaneously captures the saliency distribution and spatial priors. The model is then used to determine the best cropping rectangle. The proposed approach improves traditional saliency based cropping techniques by introducing the spatial priors, which is automatically learned through learning process. The new method is tested on images from the PASCAL08 dataset, where it outperforms previous saliency based cropping.

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Tunable and real‐time automatic interventional x‐ray collimation from semi‐supervised deep feature extraction

Lee,

Rijhwani,

Lang

et al. 2024

Medical Physics

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BackgroundThe use of endovascular procedures is becoming increasingly popular across multiple clinical domains. These procedures are generally performed under image guidance using an interventional c‐arm x‐ray system. Radiation exposure to both patients and interventional staff due to use of fluoroscopy is a health and occupational concern, but modifications to the interventional workflow to address radiation may come at the cost of procedure time or quality.PurposeInterventional x‐ray collimation is a crucial task for improving image quality as well as reducing radiation exposure to both patients and operators who work in the x‐ray domain. However, collimation is heavily underutilized due to its cumbersome nature and the difficulty of manually manipulating multiple parameters during fast‐paced interventional procedures. Additionally, the widely varying collimation preferences of interventionalists across different procedure types, procedure phases, and anatomies makes the standardizing of collimation challenging for radiation technologist support staff.MethodsAutomating collimation has the potential to bridge this gap, freeing up mental bandwidth for interventionalists and technologists and improving outcomes for patients. Here, we propose a tunable algorithm for automatic collimation based on a region‐of‐interest optimizer driven by a combination of image, system, device, and radiation based features and we illustrate its efficacy across varying personal preferences. Critically, we devise a method with a simple and easily understandable mapping between algorithm parameters and practical outcomes.ResultsWe show a real‐time implementation of this algorithm using deep feature extraction by a convolutional neural network and evaluate its performance in a custom dataset of simulated fluoroscopy and recorded fluoroscopy from clinical radial access procedures. We evaluate the effects of a practically implemented mixed supervision training strategy on model performance and show potential for radiation reduction in simulation. An uncertainty analysis indicates that the algorithm is robust to noise and anatomical variation across our clinical dataset. Clinical acceptability and quality is evaluated through a reader study with expert neuro‐interventional radiologists, with participants indicating 100% clinical acceptability, high quality ratings, and improved radiation protection over their typical practice.ConclusionsThe algorithm's modular design ensured that users' collimation requirements were met without disruption to the interventional workflow or procedure time, while exhibiting strong potential to reduce radiation risk to patients and operators. Evaluation in more varied clinical settings could support translation of this technology into the clinic.

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Automatic thumbnail cropping and its effectiveness

Cited by 379 publications

References 19 publications

Image Retargeting through Constrained Growth of Important Rectangular Partitions

Image Retargeting through Constrained Growth of Important Rectangular Partitions

Learning based thumbnail cropping

Tunable and real‐time automatic interventional x‐ray collimation from semi‐supervised deep feature extraction

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