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Author  |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Efficient Object-Class Recognition by Boosting Contextual Information |
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Miscellaneous |
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2005 |
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Pattern Recognition and Image Analysis, IbPRIA 2005, LNCS 3522:28–35 |
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Estoril (Portugal) |
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ADAS;MILAB |
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ADAS @ adas @ ASR2005b |
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554 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
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Journal Article |
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Year |
2006 |
Publication |
Pattern Recognition Letters |
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PRL |
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Volume |
27 |
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3 |
Pages |
201–209 |
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ADAS;MILAB |
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ADAS @ adas @ ASR2006 |
Serial |
643 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms |
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Journal |
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Year |
2007 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(10):1818–1833, (ISI 3,81) |
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ADAS;MILAB |
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ADAS @ adas @ ASR2007b |
Serial |
922 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Class-Specific Binaryy Correlograms for Object Recognition |
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Conference Article |
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Year |
2007 |
Publication |
British Machine Vision Conference |
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Warwick (UK) |
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BMVC’07 |
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ADAS;MILAB |
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no |
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Call Number |
ADAS @ adas @ ASR2007a |
Serial |
923 |
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Author |
Jaume Amores; David Geronimo; Antonio Lopez |
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Title |
Multiple instance and active learning for weakly-supervised object-class segmentation |
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Conference Article |
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Year |
2010 |
Publication |
3rd IEEE International Conference on Machine Vision |
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Keywords |
Multiple Instance Learning; Active Learning; Object-class segmentation. |
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In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set. |
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Hong-Kong |
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ICMV |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ AGL2010b |
Serial |
1429 |
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Author |
Jaume Amores |
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Title |
Multiple Instance Classification: review, taxonomy and comparative study |
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Journal Article |
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Year |
2013 |
Publication |
Artificial Intelligence |
Abbreviated Journal |
AI |
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Volume |
201 |
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Pages |
81-105 |
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Keywords |
Multi-instance learning; Codebook; Bag-of-Words |
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Abstract |
Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL
methods. |
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Elsevier Science Publishers Ltd. Essex, UK |
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0004-3702 |
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Notes |
ADAS; 601.042; 600.057 |
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no |
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Call Number |
Admin @ si @ Amo2013 |
Serial |
2273 |
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Author |
Jaume Amores |
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Title |
MILDE: multiple instance learning by discriminative embedding |
Type |
Journal Article |
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Year |
2015 |
Publication |
Knowledge and Information Systems |
Abbreviated Journal |
KAIS |
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Volume |
42 |
Issue |
2 |
Pages |
381-407 |
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Multi-instance learning; Codebook; Bag of words |
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Abstract |
While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. |
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Springer London |
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0219-1377 |
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ADAS; 601.042; 600.057; 600.076 |
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Call Number |
Admin @ si @ Amo2015 |
Serial |
2383 |
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Author |
Jaume Amores |
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Title |
Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
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Pages |
4246–4250 |
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Abstract |
Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families, and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that, with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases, showing in general a more consistent performance. |
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Istanbul, Turkey |
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1051-4651 |
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978-1-4244-7542-1 |
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ICPR |
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ADAS |
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Call Number |
ADAS @ adas @ Amo2010 |
Serial |
1295 |
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Author |
Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders |
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Title |
Selective Search for Object Recognition |
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Journal Article |
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Year |
2013 |
Publication |
International Journal of Computer Vision |
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IJCV |
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Volume |
104 |
Issue |
2 |
Pages |
154-171 |
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Abstract |
This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html). |
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0920-5691 |
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ALTRES;ISE |
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Call Number |
Admin @ si @ USG2013 |
Serial |
2362 |
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Author |
Jaime Moreno; Xavier Otazu; Maria Vanrell |
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Title |
Local Perceptual Weighting in JPEG2000 for Color Images |
Type |
Conference Article |
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Year |
2010 |
Publication |
5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science |
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255–260 |
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The aim of this work is to explain how to apply perceptual concepts to define a perceptual pre-quantizer and to improve JPEG2000 compressor. The approach consists in quantizing wavelet transform coefficients using some of the human visual system behavior properties. Noise is fatal to image compression performance, because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Perceptual pre-quantization reduces unperceivable details and thus improve both visual impression and transmission properties. The comparison between JPEG2000 without and with perceptual pre-quantization shows that the latter is not favorable in PSNR, but the recovered image is more compressed at the same or even better visual quality measured with a weighted PSNR. Perceptual criteria were taken from the CIWaM (Chromatic Induction Wavelet Model). |
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Joensuu, Finland |
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9781617388897 |
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CGIV/MCS |
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CIC |
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Call Number |
CAT @ cat @ MOV2010a |
Serial |
1307 |
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Author |
Jaime Moreno; Xavier Otazu; Maria Vanrell |
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Title |
Contribution of CIWaM in JPEG2000 Quantization for Color Images |
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Conference Article |
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Year |
2010 |
Publication |
Proceedings of The CREATE 2010 Conference |
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132–136 |
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The aim of this work is to explain how to apply perceptual concepts to define a perceptual pre-quantizer and to improve JPEG2000 compressor. The approach consists in quantizing wavelet transform coefficients using some of the human visual system behavior properties. Noise is fatal to image compression performance, because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Perceptual pre-quantization reduces unperceivable details and thus improve both visual impression and transmission properties. The comparison between JPEG2000 without and with perceptual pre-quantization shows that the latter is not favorable in PSNR, but the recovered image is more compressed at the same or even better visual quality measured with a weighted PSNR. Perceptual criteria were taken from the CIWaM(ChromaticInductionWaveletModel). |
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Gjovik (Norway) |
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CREATE |
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CAT @ cat @ MOV2010b |
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1308 |
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Author |
Jaime Moreno; Xavier Otazu |
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Title |
Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder |
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Conference Article |
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Year |
2011 |
Publication |
IEEE International Conference on Multimedia and Expo |
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1-6 |
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In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. The implementation of the proposed coder is developed for gray-scale and color image compression. Hi-SET compressed images are, on average, 6.20dB better than the ones obtained by other compression techniques based on the Hilbert scanning. Moreover, Hi-SET improves the image quality in 1.39dB and 1.00dB in gray-scale and color compression, respectively, when compared with JPEG2000 coder. |
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ISSN |
1945-7871 |
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978-1-61284-348-3 |
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ICME |
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CIC |
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no |
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Call Number |
Admin @ si @ MoO2011a |
Serial |
2176 |
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Author |
Jaime Moreno; Xavier Otazu |
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Title |
Image coder based on Hilbert scanning of embedded quadTrees |
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Conference Article |
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Year |
2011 |
Publication |
Data Compression Conference |
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Pages |
470-470 |
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Abstract |
In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. |
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DCC |
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CIC |
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no |
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Call Number |
Admin @ si @ MoO2011b |
Serial |
2177 |
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Author |
Jaime Lopez-Krahe; Josep Llados; Enric Marti |
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Title |
Architectural Floor Plan Analysis |
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Report |
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Year |
2000 |
Publication |
CVonline |
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Edimburg, UK |
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University of Edinburgh |
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Robert B. Fisher |
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online pdf |
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1561 |
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Author |
J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin |
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Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm |
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Journal Article |
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2013 |
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Expert Systems with Applications |
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EXWA |
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40 |
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17 |
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6707-6712 |
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Neural gas; Expert vision; Eye-tracking; Fixations |
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Abstract |
Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. |
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Admin @ si @ CRM2013 |
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2438 |
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