Signature-image-based event analysis for personal photo albums

Dao, Minh-Son; Dang-Nguyen, Duc-Tien; Natale, Francesco G. B. De

doi:10.1145/2072298.2072045

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Most detection algorithms can be used as an intermediate step for retrieving images in CBIR [99,90], such as recognized faces [187,121] and events [37], among others.…”

Section: Image Retrievalmentioning

confidence: 99%

A survey on automatic techniques for enhancement and analysis of digital photography

2013

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The fast growth in the consumer digital photography industry during the past decade has led to the acquisition and storage of large personal and public digital collections containing photos with different quality levels and redundancy, among other aspects. This naturally increased the difficulty in selecting or modifying those photos. Within the above context, this survey focuses on systematically reviewing the state-of-art on techniques for the enhancement and analysis of digital photos. Nevertheless, it is not within the scope of this survey to review image quality metrics for evaluating degradation due to compression, digital sensor noise, and affine issues. Assuming the photos have good quality in those aspects, this review is centered on techniques that might be useful to automate the task of selecting photos from large collections or to enhance the visual aspect of imperfect photos by using some perceptual measure.

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“…Most detection algorithms can be used as an intermediate step for retrieving images in CBIR [99,90], such as recognized faces [187,121] and events [37], among others.…”

Section: Image Retrievalmentioning

confidence: 99%

A survey on automatic techniques for enhancement and analysis of digital photography

2013

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“…Annotating personal photo albums is an emerging trend in photo organizing services [8]. A method for hierarchical photo organization into topics and topic-related categories on a smartphone is proposed in [17] based on integration of convolutional neural network (CNN) and topic modeling for image classification.…”

Section: Introductionmentioning

confidence: 99%

“…The task of event recognition in the personal photo collections is not to recognize event in individual photo but in the whole album.The events and sub-events of the sub-sequence photos are identified in [8] by integrating the optimized linear programming with the color descriptor of the signature image. The Stopwatch Hidden Markov Models were applied in [5] by treating the photos in an album as sequential data.…”

Section: Introductionmentioning

confidence: 99%

Event Recognition with Automatic Album Detection based on Sequential Processing, Neural Attention and Image Captioning

Savchenko

2019

Preprint

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In this paper a new formulation of event recognition task is examined: it is required to predict event categories in a gallery of images, for which albums (groups of photos corresponding to a single event) are unknown. We propose the novel two-stage approach. At first, features are extracted in each photo using the pre-trained convolutional neural network. These features are classified individually. The scores of the classifier are used to group sequential photos into several clusters. Finally, the features of photos in each group are aggregated into a single descriptor using neural attention mechanism. This algorithm is optionally extended to improve the accuracy for classification of each image in an album. In contrast to conventional fine-tuning of convolutional neural networks (CNN) we proposed to use image captioning, i.e., generative model that converts images to textual descriptions. They are one-hot encoded and summarized into sparse feature vector suitable for learning of arbitrary classifier. Experimental study with Photo Event Collection and Multi-Label Curation of Flickr Events Dataset demonstrates that our approach is 9-20% more accurate than event recognition on single photos. Moreover, proposed method has 13-16% lower error rate than classification of groups of photos obtained with hierarchical clustering. It is experimentally shown that the image captions trained on Conceptual Captions dataset can be classified more accurately than the features from object detector, though they both are obviously not as rich as the CNN-based features. However, it is possible to combine our approach with conventional CNNs in an ensemble to provide the stateof-the-art results for several event datasets.

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Robust event discovery from photo collections using Signature Image Bases (SIBs)

Dao

Dang-Nguyen

Natale

2012

Multimed Tools Appl

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Signature-image-based event analysis for personal photo albums

Cited by 5 publications

References 10 publications

A survey on automatic techniques for enhancement and analysis of digital photography

A survey on automatic techniques for enhancement and analysis of digital photography

Event Recognition with Automatic Album Detection based on Sequential Processing, Neural Attention and Image Captioning

Robust event discovery from photo collections using Signature Image Bases (SIBs)

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