From Re-identification to Identity Inference: Labeling Consistency by Local Similarity Constraints

Karaman, Svebor; Lisanti, Giuseppe; Bagdanov, Andrew D.; Bimbo, Alberto Del

doi:10.1007/978-1-4471-6296-4_14

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…In this latter work, images of the same person are recognized by minimizing the salience matching cost. Conditional Random Fields have been exploited in [14] relying on a nearest neighbors topology between all the images of a dataset. The same authors also proposed a semi-supervised techniques where the data cost potential is estimated from SVM scores [13].…”

Section: Related Workmentioning

confidence: 99%

Matching People across Camera Views using Kernel Canonical Correlation Analysis

Lisanti

Masi

Bimbo

2014

Proceedings of the International Conference on Distributed Smart Cameras

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102

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Matching people across views is still an open problem in computer vision and in video surveillance systems. In this paper we address the problem of person re-identification across disjoint cameras by proposing an efficient but robust kernel descriptor to encode the appearance of a person. The matching is then improved by applying a learning technique based on Kernel Canonical Correlation Analysis (KCCA) which finds a common subspace between the proposed descriptors extracted from disjoint cameras, projecting them into a new description space. This common description space is then used to identify a person from one camera to another with a standard nearest-neighbor voting method. We evaluate our approach on two publicly available datasets for re-identification (VIPeR and PRID), demonstrating that our method yields state-of-the-art performance with respect to recent techniques proposed for the re-identification task.

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Section: Related Workmentioning

confidence: 99%

Matching People across Camera Views using Kernel Canonical Correlation Analysis

Lisanti

Masi

Bimbo

2014

Proceedings of the International Conference on Distributed Smart Cameras

Self Cite

102

View full text Add to dashboard Cite

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“…where d a i is an appearance descriptor consisting of RGB and HS color histograms computed on overlapping horizontal stripes and the HoG (Histogram of Oriented Gradients) descriptor [6] as proposed for person re-identification in [10],…”

Section: Identity Modelingmentioning

confidence: 99%

Passive Profiling and Natural Interaction Metaphors for Personalized Multimedia Museum Experiences

Karaman

Bagdanov

D'Amico

et al. 2013

New Trends in Image Analysis and Processing – ICIAP 2013

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Museums must balance the amount of information given on individual pieces or exhibitions in order to provide sufficient information to aid visitor understanding. At the same time they must avoid cluttering the environment and reducing the enjoyment of the exhibit. Moreover, each visitor has different interests and each might prefer more (or less) information on different artworks depending on their individual profile of interest. Finally, visiting a museum should not be a closed experience but a door opened onto a broader context of related artworks, authors, artistic trends, etc. In this paper we describe the MNEMOSYNE system that attempts to provide such a museum experience. Based on passive observation of visitors, the system builds a profile of the artworks of interest for each visitor. These profiles of interest are then used to personalize content delivery on an interactive table. The natural user interface on the interactive table uses the visitor's profile, a museum content ontology and a recommendation system to personalize the user's exploration of available multimedia content. At the end of their visit, the visitor can take home a personalized summary of their visit on a custom mobile application. In this article we describe each component of our approach as well as the first field trials of our prototype system built and deployed at our permanent exhibition space at Le Murate 1 in the city of Florence.

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