Generating diverse and representative image search results for landmarks

Kennedy, Lyndon; Нааман, Мор

doi:10.1145/1367497.1367539

Cited by 347 publications

(239 citation statements)

References 19 publications

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“…Specifically, they were interested in selecting metadata from image collections that might best describe a geographical region. Similar work by Kennedy and Naaman [2], focused on extracting textual descriptions of geographical features, specifically landmarks, from large collections of Flickr metadata. Tags are clustered based on location, and tags are selected using a tf-idf approach, so as to correlate with nearby landmarks.…”

Section: Previous Workmentioning

confidence: 99%

A machine learning approach to determining tag relevance in geotagged Flickr imagery

Hughes

O’Connor

Jones

2012

2012 13th International Workshop on Image Analysis for Multimedia Interactive Services

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We present a novel machine learning based approach to determining the semantic relevance of community contributed image annotations for the purposes of image retrieval. Current large scale community image retrieval systems typically rely on human annotated tags which are subjectively assigned and may not provide useful or semantically meaningful labels to the images. Homogeneous tags which fail to distinguish between are a common occurrence, which can lead to poor search effectiveness on this data. We described a method to improve text based image retrieval systems by eliminating generic or non relevant image tags. To classify tag relevance, we propose a novel feature set based on statistical information available for each tag within a collection of geotagged images harvested from Flickr. Using this feature set machine learning models are trained to classify the relevance of each tag to its associated image. The goal of this process is to allow for rich and accurate captioning of these images, with the objective of improving the accuracy of text based image retrieval systems. A thorough evaluation is carried out using a human annotated benchmark collection of Flickr tags.

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Section: Previous Workmentioning

confidence: 99%

A machine learning approach to determining tag relevance in geotagged Flickr imagery

Hughes

O’Connor

Jones

2012

2012 13th International Workshop on Image Analysis for Multimedia Interactive Services

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“…Kennedy and Naaman [14] presented a method to search representative landmark images from a large collection of geotagged images. This method uses tags and the geographical location representing a landmark.…”

Section: Combined Analysis Of Geographical Context and Visual Contentmentioning

confidence: 99%

Geotag propagation in social networks based on user trust model

Ivanov

Vajda

Lee

et al. 2010

Multimed Tools Appl

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In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmarks.

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“…In this paper strong focus is placed on low level features of the images. In [7], the author generates diverse and representative image search results for landmarks based on context-and content-based tools. To do that the author used location and other metadata as well as tags associated with images, and the images' visual features.…”

Section: Introductionmentioning

confidence: 99%

Generating Representative Sets and Summaries for Large Collection of Images Using Image Cropping Techniques and Result Comparison

Al‐Mamun¹,

Gandhi²,

Noori³

2015

ijacsa

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Abstract-The collection of photos hosted on photo archives and social networking sites has been increasing exponentially. It is really hard to get the summary of a large image set without browsing through the entire collection. In this paper two different techniques of image cropping (random windows technique and sequential windows technique) have been proposed to generate effective representative sets. A ranking mechanism has been also proposed for finding the best representative set.

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Generating diverse and representative image search results for landmarks

Cited by 347 publications

References 19 publications

A machine learning approach to determining tag relevance in geotagged Flickr imagery

A machine learning approach to determining tag relevance in geotagged Flickr imagery

Geotag propagation in social networks based on user trust model

Generating Representative Sets and Summaries for Large Collection of Images Using Image Cropping Techniques and Result Comparison

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