Multiple Exemplar-Based Facial Image Retrieval Using Independent Component Analysis
IBM1
TL;DR: A content-based image retrieval system where multiple query examples can be used to indicate the need to retrieve not only images similar to the individual examples, but also those images which actually represent a combination of the content of query images.
Abstract: In this paper, we design a content-based image retrieval system where multiple query examples can be used to indicate the need to retrieve not only images similar to the individual examples, but also those images which actually represent a combination of the content of query images. We propose a scheme for representing content of an image as a combination of features from multiple examples. This scheme is exploited for developing a multiple example-based retrieval engine. We have explored the use of machine learning techniques for generating the most appropriate feature combination scheme for a given class of images. The combination scheme can be used for developing purposive query engines for specialized image databases. Here, we have considered facial image databases. The effectiveness of the image retrieval system is experimentally demonstrated on different databases
Citations
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12 Sep 2009
TL;DR: This paper classified image retrieval into text based and content based, including the newly growing ontology based image retrieval system as one focus and addressed the challenges, techniques and evaluation methods used in image retrieval systems through a detailed look of most recent works.
Abstract: In the past decades the advancement in the area of database management systems shifts towards multimedia. Multimedia information is very expressive, self explanatory, narrative, etc. Now a day the development of digital media, advanced network infrastructure and the easily available consumer electronics makes the multimedia revolution to run in an alarming rate [4] Inline with the advancement of database technology that incorporates multimedia data, an open question that always rose in the technology is how to retrieve/search images in the multimedia databases. There are a huge number of research works focusing on the searching mechanisms in image databases for efficient retrieval and tried to give supplementary suggestions on the overall systems. The growing of digital medias (digital camera, digital video, digital TV, e-book, cell phones, etc.) gave rise to the revolution of very large multimedia databases, in which the need of efficient storage, organization and retrieval of multimedia contents came into question. Among the multimedia data, this survey paper focuses on the different methods (approaches) and their evaluation techniques used by many of recent research works on image retrieval system. Many researchers develop and use lots of approaches towards image retrieval. This paper, in general, classified image retrieval into text based and content based, including the newly growing ontology based image retrieval system as one focus. We address, in this paper, the challenges, techniques and evaluation methods used in image retrieval systems through a detailed look of most recent works
51 citations
TL;DR: The approach demonstrates that, in addition to a single image-based query, a compound query with multiple query images can be used to search for images with compounding feature content and allows for online processing without the need to learn query images.
29 citations
TL;DR: The scheme presents the extension of distance based hashing to kernel space for generating the indexing structure based on similarity in kernel space using the concept of multiple kernel learning to incorporate multiple features for defining the image indexing space.
Abstract: The paper presents a novel feature based indexing scheme for image collections. The scheme presents the extension of distance based hashing to kernel space for generating the indexing structure based on similarity in kernel space. The objective of the scheme is to incorporate multiple features for defining the image indexing space using the concept of multiple kernel learning. However, the indexing problems are defined with unique learning objective; therefore, a novel application of genetic algorithm is presented for the optimization task. The extensive evaluation of the proposed concept is performed for developing word based document indexing application of Devanagari, Bengali, and English scripts. In addition, the efficacy of the proposed concept is shown by experimental evaluations on handwritten digits and natural image collection.
22 citations
Cites background from "Multiple Exemplar-Based Facial Imag..."
...[23] defined feature combination at local neighborhood level for face image retrieval....
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29 Oct 2007
TL;DR: A new technique of facial-image retrieval using constrained independent component analysis (cICA) that works completely online, and can cater for queries formulated from both single and multiple examples, for achieving higher accuracy.
Abstract: In this work we present a new technique of facial-image retrieval using constrained independent component analysis (cICA). We have employed cICA for the online extraction of those independent components from the entire database which bear some similarity to the query-images. Instead of using any offline learning mechanism or feature extraction technique, our system works completely online. It can cater for queries formulated from both single and multiple examples, for achieving higher accuracy. For compound queries, instead of treating each query-image independently, the system is capable of finding images similar not only to the individual query-images, but also to their different combinations
11 citations
Cites background or methods from "Multiple Exemplar-Based Facial Imag..."
...Similarly, [ 2 ] uses multiple facial images to retrieve images similar to the independent query-images as well as to their combinations....
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...A hard similarity evaluation was used, and only the retrieved images pertaining to the same individual as depicted in the query were considered relevant as opposed to [ 2 ], where it was assumed that user feedback is available....
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...There is also no need to store additional feature information or the need for any offline learning as in [ 2 ]....
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...Recent work on facial image retrieval [ 2 ] has focused on representing facial images as a collection of local independent components....
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...It does not decompose the query images or the database images to windows as in [ 2 ] or uses PCA [13] for dimension reduction; so the chances of any information loss are minimal....
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29 Oct 2007
TL;DR: A new approach in the automated iris and gaze detection problem is presented, using the chrominance of low resolution webcam data, which exhibits high performance, circumventing the variability of the lighting conditions and/or user's facial characteristics.
Abstract: Human-computer interaction requires efficient acquisition of the relevant information from the user. This poses high accuracy in the performance of the relevant systems, especially when visual information is used as the basic means to capture the user's information. At the same time, speed and efficiency under a variety of conditions (e.g, resolution, luminance) is required. From this perspective, a new approach in the automated iris and gaze detection problem is presented here, using the chrominance of low resolution webcam data. The algorithms developed, when combined with a face tracker, exhibit high performance, circumventing the variability of the lighting conditions and/or user's facial characteristics.
9 citations
References
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Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Abstract: We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
14,562 citations
"Multiple Exemplar-Based Facial Imag..." refers background in this paper
...It may be mentioned here that independent components and nonlinear variants of principal components [29], [30] have already been used as the descriptors of image content, particularly in the domain of face recognition [14], [31]–[35]....
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TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Abstract: This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algorithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.
13,037 citations
07 Jul 2001
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Abstract: This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algo- rithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a "cascade" which allows back- ground regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection perfor- mance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.
10,592 citations
"Multiple Exemplar-Based Facial Imag..." refers background in this paper
...Viola and Jones [ 20 ] provided a computationally cheaper model for face image retrieval where the notion of an “integral image” was introduced....
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