In this paper we present a novel approach for personal photo album management. Pictures are analyzed and
described in three representation spaces, namely, faces, background and time of capture. Faces are automatically
detected and rectified using a probabilistic feature extraction technique. Face representation is then produced
by computing PCA (Principal Component Analysis). Backgrounds are represented with low-level visual features
based on RGB histogram and Gabor filter bank. Temporal data is obtained through the extraction of EXIF
(Exchangeable image file format) data. Each image in the collection is then automatically organized using a
mean-shift clustering technique. While many systems manage faces and typically allow queries about them we
use a common approach to manage multiple aspects, that is, queries regarding people, time and background
are dealt with in a homogenous way. We report experimental results on a realistic set, i.e., a personal photo
album, of about 2000 images where automatic detection and rectification of faces lead to approximately 800
faces. Significance of clustering has been evaluated and results are very interesting.
In this paper we propose a novel feature based on SIFT (Scale Invariant Feature Transform) algorithm1 for
the robust representation of local visual contents. SIFT features have raised much interest for their power of
description of visual content characterizing punctual information against variation of luminance and change of
viewpoint and they are very useful to capture local information. For a single image hundreds of keypoints are
found and they are particularly suitable for tasks dealing with image registration or image matching. In this
work we stretched the spatial coverage of descriptors creating a novel feature as composition of keypoints present
in an image region while maintaining the invariance properties of SIFT descriptors. The number of descriptors
is reduced, limiting the computational weight, and at the same time a more abstract descriptor is achieved. The
new feature is therefore suitable at describing objects and characteristic image regions.
We tested the retrieval performance with a dataset used to test PCA SIFT2 and image matching capability
among images processed with affine transformations. Experimental results are reported.
We propose a novel approach for the automatic representation
of pictures achieving a more effective organization of personal
photo albums. Images are analyzed and described in multiple
representation spaces, namely, faces, background, and time of capture.
Faces are automatically detected, rectified, and represented,
projecting the face itself in a common low-dimensional eigenspace.
Backgrounds are represented with low-level visual features based
on an RGB histogram and Gabor filter bank. Faces, time, and background
information of each image in the collection is automatically
organized using a mean-shift clustering technique. Given the particular
domain of personal photo libraries, where most of the pictures
contain faces of a relatively small number of different individuals,
clusters tend to be semantically significant besides containing visually
similar data. We report experimental results based on a data set
of about 1000 images where automatic detection and rectification of
faces lead to approximately 400 faces. Significance of clustering
has been evaluated, and results are very encouraging.
In this paper we present a novel approach to personal photo album management allowing the end user to efficiently access the collection without any need for tedious manual annotation or indexing of the photos. The proposed work exploits methods and technology from the field of computer vision and pattern recognition for
face detection, face representation and image annotation to automatically create description of images useful for
content-based searching and retrieval. In fact, even if most of the used techniques are not reliable enough to address the general problem of content-based image retrieval, we show that, in a limited domain such as the one of personal photo album, it is possible to obtain results that improve the browsing capabilities of current photo album management systems. In particular, starting from the observation that most personal photos depict a usually small number of people in a relatively small number of different context (indoor, outdoor, beach, mountain, city, etc...) we propose the use of automatic techniques to index images based on who is present in the scene and on the context where the picture was taken. Experiments on a personal photo collection of about a thousand images proved that relatively simple content-based techniques lead to surprisingly good results in term of easyness of user access to the data.
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