A scalable service for photo annotation, sharing, and search

Lee, Benjamin N.; Chen, Wen-Yen; Chang, Edward Yi

doi:10.1145/1180639.1180787

Cited by 13 publications

(7 citation statements)

References 10 publications

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“…[2]. Recently, some authors proposed to combine pixel-level image information with the meta-data often associated to images, like EXIF camera informations [4,12,19] and user-generated tags [12].…”

Section: Related Work On Indoor-outdoor Classificationmentioning

confidence: 99%

Exploiting Depth Information for Indoor-Outdoor Scene Classification

Pillai

Satta

Fumera

et al. 2011

Image Analysis and Processing – ICIAP 2011

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Abstract.A rapid diffusion of stereoscopic image acquisition devices is expected in the next years. Among the different potential applications that depth information can enable, in this paper we focus on its exploitation as a novel information source in the task of scene classification, and in particular to discriminate between indoor and outdoor images. This issue has not been addressed so far in the literature, probably because the extraction of depth information from two-dimensional images is a computationally demanding task. However, new-generation stereo cameras will allow a very fast computation of depth maps. We experimentally show that depth information alone provides a discriminant capability between indoor and outdoor images close to state-of-the art methods based on colour, edge and texture information, and that it allows to improve their performance, when it is used as an additional information source.

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Section: Related Work On Indoor-outdoor Classificationmentioning

confidence: 99%

Exploiting Depth Information for Indoor-Outdoor Scene Classification

Pillai

Satta

Fumera

et al. 2011

Image Analysis and Processing – ICIAP 2011

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“…Certain searching methods use text annotation to find photos, but most digital photos do not include annotation. Thus, some methods were proposed that annotate digital photos after searching [41], probabilistic analysis [45] or content-based clustering [25] which extracts semantic meaning for further usage. Cui's EasyAlbum also annotate photos with partial clustering that gathers faces with similar scene/facial features [15].…”

Section: Previous Workmentioning

confidence: 99%

A clustering method for concurrent photos obtained from multiple cameras using max-flow network model

Jang

Cho

2011

Multimedia Systems

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With the popularization of digital cameras, the use of several cameras by group photographers at the same event is becoming common. Photographers can share their contents and even take pictures of each other. So it is becoming important to manage concurrent photos from multiple cameras in order to classify many accumulated photos into proper clusters. In this paper, we propose a novel photo clustering method based on the max-flow network algorithm, and we visualize a network graph for cluster verification. To apply our algorithm, input concurrent photos are used to create an edge-weighted graph structure. In order to transform the photo clustering problem into a graph partition one, first we need to construct an Augmented Concurrent photo Graph (ACG) and then rewrite our original problem in terms of the graph partition one using the min-cut max-flow network model. The previous methods dealt with photo clustering as a 1-D problem using a linear partition. But we consider clustering for concurrent group photos as a 2-D partition based on other users' photo contents. Each photo is used to create a node and similarities between photos are used to create the edge weights (capacities) of the network. We partition the network into two subgraphs according to the min-cut, which represents the weakest edge connections between the photos. Using repeated graph partitions for each subgraph (sub-network), we can obtain suitable subgraphs corresponding to photo clusters. The graph construction or partition can be adjusted according to user preferences in order to obtain the intended results.Keywords Photo clustering Á Maximum flow Á Network model Á EXIF MotivationCurrently, the digital camera is so popular that many households have one or more. Moreover, the main storage medium of the digital camera, flash memory, is becoming cheaper as its capacity increases. In contrast to the analogue film camera, after shooting, digital camera users can select the photos they want while still in front of their computer or camera. The convenience of digital cameras makes it easy for people to take photographs regardless of cost or redundancy. Therefore, digital camera owners tend to take more photos than film camera owners.There are two issues in digital photo management. The first is dealing with the massive number of highly related photos. Usually a user takes at least a hundred photos each time they travel or attend an event, and an intensive user may take thousands of photos. Since the number of photos a user must classify is so large, most people find this boring and time-consuming work. The previous studies on photo management focused on this point and are designed for clustering photos from a single user's photo collection via temporal or spatial analysis. But the patterns of concurrent group photos can vary according to the photographers and so we have to deal with various kinds of input photos.The second issue we focus on is extracting the relationships between group photos. With the increased popularity of digital cameras, it is in...

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“…Due to the widespread diffusion of digital photography, several studies addressed the problem of semi-automatic personal photo collection management [4] [5] [6]. Most of the effort has been devoted to finding techniques to help the user in annotating the collection.…”

Section: Related Workmentioning

confidence: 99%

Mobile Interface for Content-Based Image Management

Cascia¹,

Morana²,

Sorce³

2010

2010 International Conference on Complex, Intelligent and Software Intensive Systems

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Abstract-People make more and more use of digital image acquisition devices to capture screenshots of their everyday life. The growing number of personal pictures raise the problem of their classification. Some of the authors proposed an automatic technique for personal photo album management dealing with multiple aspects (i.e., people, time and background) in a homogenous way. In this paper we discuss a solution that allows mobile users to remotely access such technique by means of their mobile phones, almost from everywhere, in a pervasive fashion. This allows users to classify pictures they store on their devices. The whole solution is presented, with particular regard to the user interface implemented on the mobile phone, along with some experimental results.

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A scalable service for photo annotation, sharing, and search

Cited by 13 publications

References 10 publications

Exploiting Depth Information for Indoor-Outdoor Scene Classification

Exploiting Depth Information for Indoor-Outdoor Scene Classification

A clustering method for concurrent photos obtained from multiple cameras using max-flow network model

Mobile Interface for Content-Based Image Management

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