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"numChildren": 1 }, "data": { "key": "WDTITXUP", "version": 1, "itemType": "conferencePaper", "title": "PCA-SIFT: a more distinctive representation for local image descriptors", "creators": [ { "creatorType": "author", "firstName": "", "lastName": "Yan Ke" }, { "creatorType": "author", "firstName": "R.", "lastName": "Sukthankar" } ], "abstractNote": "

Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid (June 2003) recently evaluated a variety of approaches and identified the SIFT [D. G. Lowe, 1999] algorithm as being the most resistant to common image deformations. This paper examines (and improves upon) the local image descriptor used by SIFT. Like SIFT, our descriptors encode the salient aspects of the image gradient in the feature point's neighborhood; however, instead of using SIFT's smoothed weighted histograms, we apply principal components analysis (PCA) to the normalized gradient patch. Our experiments demonstrate that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation. We also present results showing that using these descriptors in an image retrieval application results in increased accuracy and faster matching.

", "proceedingsTitle": "Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on", "conferenceName": "Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on", "publisher": "", "place": "", "date": "2004", "eventPlace": "", "volume": "2", "issue": "", "numberOfVolumes": "", "pages": "II-506-II-513 Vol.2", "series": "", "seriesNumber": "", "DOI": "10.1109/CVPR.2004.1315206", "ISBN": "1063-6919", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "PCA-SIFT", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "feature extraction", "type": 1 }, { "tag": "image deformations", "type": 1 }, { "tag": "image gradient", "type": 1 }, { "tag": "image registration", "type": 1 }, { "tag": "image representation", "type": 1 }, { "tag": "image retrieval", "type": 1 }, { "tag": "image retrieval application", "type": 1 }, { "tag": "local feature detection", "type": 1 }, { "tag": "local image descriptor", "type": 1 }, { "tag": "object recognition", "type": 1 }, { "tag": "object recognition algorithms", "type": 1 }, { "tag": "principal component analysis", "type": 1 }, { "tag": "principal components analysis", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2011-04-20T22:48:20Z", "dateModified": "2011-04-20T22:48:20Z" } }, { "key": "9KRT3TDA", "version": 1, "library": { "type": "group", "id": 43751, "name": "ECE513 Project", "links": { "alternate": { "href": "https://www.zotero.org/groups/ece513_project", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43751/items/9KRT3TDA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ece513_project/items/9KRT3TDA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 516216, "username": "hnkulkarni", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/hnkulkarni", "type": "text/html" } } }, "creatorSummary": "Belongie et al.", "parsedDate": "2001", "numChildren": 1 }, "data": { "key": "9KRT3TDA", "version": 1, "itemType": "conferencePaper", "title": "Matching shapes", "creators": [ { "creatorType": "author", "firstName": "S.", "lastName": "Belongie" }, { "creatorType": "author", "firstName": "J.", "lastName": "Malik" }, { "creatorType": "author", "firstName": "J.", "lastName": "Puzicha" } ], "abstractNote": "

We present a novel approach to measuring similarity between shapes
and exploit it for object recognition. In our framework, the measurement
of similarity is preceded by (1) solving for correspondences between
points on the two shapes, (2) using the correspondences to estimate an
aligning transform. In order to solve the correspondence problem, we
attach a descriptor, the shape context, to each point. The shape context
at a reference point captures the distribution of the remaining points
relative to it, thus offering a globally discriminative
characterization. Corresponding points on two similar shapes will have
similar shape contexts, enabling us to solve for correspondences as an
optimal assignment problem. Given the point correspondences, we estimate
the transformation that best aligns the two shapes; regularized
thin-plate splines provide a flexible class of transformation maps for
this purpose. Dis-similarity between two shapes is computed as a sum of
matching errors between corresponding points, together with a term
measuring the magnitude of the aligning transform. We treat recognition
in a nearest-neighbor classification framework. Results are presented
for silhouettes, trademarks, handwritten digits and the COIL dataset

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A pictorial structure is a collection of parts arranged in a
deformable configuration. Each part is represented using a simple
appearance model and the deformable configuration is represented by
spring-like connections between pairs of parts. While pictorial
structures were introduced a number of years ago, they have not been
broadly applied to matching and recognition problems. This has been due
in part to the computational difficulty of matching pictorial structures
to images. In this paper we present an efficient algorithm for finding
the best global match of a pictorial stucture to an image. With this
improved algorithm, pictorial structures provide a practical and
powerful framework for quantitative descriptions of objects and scenes,
and are suitable for many generic image recognition problems. We
illustrate the approach using simple models of a person and a car

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