An interactive system for mental face retrieval

Fang, Yuchun; Geman, Donald; Boujemaa, Nozha

doi:10.1145/1101826.1101858

Cited by 9 publications

(4 citation statements)

References 21 publications

(19 reference statements)

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“…From an image modeling perspective, the Kansei methodology should encompass methods that associate low-level image features with human feelings and impressions. Another work [30] attempts to model the target image within the mind of a user with respect to a face retrieval task. In the cited work, a relevance feedback-based approach is used to learn a distribution over the image database that represents the mental image of the user, and to use this distribution for retrieval.…”

Section: Aesthetics Emotions and Image Retrievalmentioning

confidence: 99%

Aesthetics and Emotions in Images

Joshi

Datta

Fedorovskaya

et al. 2011

IEEE Signal Process. Mag.

361

163

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I n this tutorial, we define and discuss key aspects of the problem of computational inference of aesthetics and emotion from images. We begin with a background discussion on philosophy, photography, paintings, visual arts, and psychology. This is followed by introduction of a set of key computational problems that the research community has been striving to solve and the computational framework required for solving them. We also describe data sets available for performing assessment and outline several real-world applications where research in this domain can be employed. A significant number of papers that have attempted to solve problems in aesthetics and emotion inference are surveyed in this tutorial. We also discuss future directions that researchers can pursue and make a strong case for seriously attempting to solve problems in this research domain.

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Section: Aesthetics Emotions and Image Retrievalmentioning

confidence: 99%

Aesthetics and Emotions in Images

Joshi

Datta

Fedorovskaya

et al. 2011

IEEE Signal Process. Mag.

361

163

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show abstract

“…Our framework is the classical query by example. Even though this framework is known to have some limitation (see for instance [21]), it is nevertheless usually one of the building blocks of a querying system and it enables us to address the problem we are concerned with : how to decide which answers are meaningful when querying a very large amount of data. More precisely, we tackle the following problem: which images from a database should be matched with a query image?…”

Section: Distancementioning

confidence: 99%

Adaptive image retrieval based on the spatial organization of colors

Hurtut¹,

Gousseau²,

Schmitt³

2008

Computer Vision and Image Understanding

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This work proposes to compare the spatial organization of colors between images through a global optimization procedure relying on the Earth Mover's Distance. The resulting distance is applied to image retrieval. Unlike most region-based retrieval systems, no segmentation of images is needed for the query. We then address the decision stage of the retrieval, that is the problem of automatically deciding which images from a database match a query. To this aim, we make use of an a contrario method. Two images are matched if their proximity is unlikely to be due to chance; more precisely, a matching threshold on distances is computed by controlling the average number of false matchings in an unsupervised way. This threshold is adaptive, yielding different numbers of result images depending on the query and the database.

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“…Fuzzy clustering and inference methods were developed by Conilione and Wang to derive membership degree for each semantic label to a new image, which served to better annotate face images [7]. Fang et al [8] utilized Bayesian inference and relevance feedback to retrieval mental image from large scale database. Vikram et al [9] proposed to preserve spatial scattering of relevant dominant points on faces, moreover, this information was put into kd-tree index structure for efficient FIR.…”

Section: Introductionmentioning

confidence: 99%

Analysis on Features and Metrics in Face Image Retrieval System

Fang

2013

Biometric Recognition

Self Cite

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Abstract. In the field of face recognition (FR) and face image retrieval (FIR), features and metrics have received great attention in recent years due to their direct influence on the performance of a FR/FIR system. In this paper, we analyze the two factors for FIR in following steps. First, the face images are aligned to the same size, moreover, their illumination is balanced. Second, we extract classic features widely used in face recognition and retrieval, then utilize them in feature matching with different metrics. At last, face retrieval is performed based on the distances calculated with multiple metrics. We evaluate the efficiency of features and metrics by face retrieval in Face Recognition Grand Challenge (FRGC) database. Experimental results not only serves to select features and metrics for FIR, they also demonstrate that the two variables affect FR and FIR in different ways.

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An interactive system for mental face retrieval

Cited by 9 publications

References 21 publications

Aesthetics and Emotions in Images

Aesthetics and Emotions in Images

Adaptive image retrieval based on the spatial organization of colors

Analysis on Features and Metrics in Face Image Retrieval System

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