FaceTracer: A Search Engine for Large Collections of Images with Faces

Kumar, Neeraj; Belhumeur, Peter N.; Nayar, Shree K.

doi:10.1007/978-3-540-88693-8_25

Cited by 298 publications

(269 citation statements)

References 11 publications

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“…computer vision tasks like image search [17,18] and classification [19,20,15,2]. In this work, we show that attributes can be used for achieving more accurate clusterings when using semi-supervised clustering algorithms.…”

Section: Introductionmentioning

confidence: 74%

Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations

Lad

Parikh

2014

Computer Vision – ECCV 2014

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(ABSTRACT)Unsupervised image clustering is a challenging and often ill-posed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion itself may depend on (unknown) user preferences. Semi-supervised approaches such as distance metric learning and constrained clustering thus leverage user-provided annotations indicating which pairs of images belong to the same cluster (must-link) and which ones do not (cannot-link). These approaches require many such constraints before achieving good clustering performance because each constraint only provides weak cues about the desired clustering.In this work, we propose to use image attributes as a modality for the user to provide more informative cues. In particular, the clustering algorithm iteratively and actively queries a user with an image pair. Instead of the user simply providing a must-link/cannot-link constraint for the pair, the user also provides an attribute-based reasoning e.g. "these two images are similar because both are natural and have still water" or "these two people are dissimilar because one is way older than the other". Under the guidance of this explanation, and equipped with attribute predictors, many additional constraints are automatically generated.We demonstrate the effectiveness of our approach by incorporating the proposed attributebased explanations in three standard semi-supervised clustering algorithms: Constrained K-Means, MPCK-Means, and Spectral Clustering, on three domains: scenes, shoes, and faces, using both binary and relative attributes.

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Section: Introductionmentioning

confidence: 74%

Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations

Lad

Parikh

2014

Computer Vision – ECCV 2014

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“…We compare its performance against our ATTRIBUTE PIVOTS and the strongest baseline, TOP. We issue 50 queries for Shoes-1k (a random 1000-image subset of Shoes), Scenes, and Faces-Unique (1 image for each of 200 individuals from the original PubFig dataset [10], using the 6 most predictable attributes). All methods share one simulated feedback statement at iteration 0, which we do not plot.…”

Section: Results With Live Usersmentioning

confidence: 99%

“…We validate with three public datasets: Shoes [1], with the attributes from [8] (14,658 images and 10 attributes); outdoor Scenes (2,688 images and 6 attributes); and PubFig celebrity Faces [10] (772 images and 11 attributes). We concatenate GIST and color features for Shoes and Faces, and GIST alone for Scenes.…”

Section: Methodsmentioning

confidence: 99%

“…Visual attributes are semantic properties of objects (e.g., "fuzzy", "plastic") that serve as a middle ground between low-level features (e.g., color, texture) and high-level categories. Attributes (or "concepts", their counterpart in multimedia retrieval) are known to provide an effective representation for image search [15,10,20,22,4,8,25,7], especially since they permit content-based keyword queries [10,22,7]. While often treated as categorical ("is smiling" vs. "is not smiling"), attributes can more generally be modeled as continuous or relative properties ("is smiling more than X") [16,21].…”

Section: Related Workmentioning

confidence: 99%

“…Using standard features and kernels, we find that 75% of held-out human comparisons are preserved by attribute predictors trained with ∼200 pairs. Thus, they are quite reliable; more elaborate features [10] or learning algorithms [12] would likely improve them even further.…”

Section: Relative Attribute Predictionsmentioning

confidence: 99%

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Attribute Pivots for Guiding Relevance Feedback in Image Search

Kovashka

Grauman

2013

2013 IEEE International Conference on Computer Vision

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In interactive image search, a user iteratively refines his results by giving feedback on exemplar images. Active selection methods aim to elicit useful feedback, but traditional approaches suffer from expensive selection criteria and cannot predict informativeness reliably due to the imprecision of relevance feedback. To address these drawbacks, we propose to actively select "pivot" exemplars for which feedback in the form of a visual comparison will most reduce the system's uncertainty. For example, the system might ask, "Is your target image more or less crowded than this image?" Our approach relies on a series of binary search trees in relative attribute space, together with a selection function that predicts the information gain were the user to compare his envisioned target to the next node deeper in a given attribute's tree. It makes interactive search more efficient than existing strategies-both in terms of the system's selection time as well as the user's feedback effort.

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Facial Soft Biometrics for Person Recognition

Dantcheva

2012

Signal and Image Processing for Biometrics

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FaceTracer: A Search Engine for Large Collections of Images with Faces

Cited by 298 publications

References 11 publications

Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations

Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations

Attribute Pivots for Guiding Relevance Feedback in Image Search

Facial Soft Biometrics for Person Recognition

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