Product Quantization for Nearest Neighbor Search Jégou, H; Douze, M; Schmid, C
IEEE transactions on pattern analysis and machine intelligence,
2011-Jan., 2011, 2011-Jan, 2011-01-00, 20110101, 2011-01, Letnik:
33, Številka:
1
Journal Article
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This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to ...quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors.
Histiocytoma or dermatofibroma (DF) is a common benign skin tumour with several clinical and histopathological variants. Sebaceous induction overlying a dermatofibroma is rare and infrequently ...reported. Using a detailed clinical case, herein the semiological and dermoscopic features of this lesion are described and illustrated, and the differential diagnoses presented.
A 52-year-old man consulted for a physical examination, which revealed a firm papular lesion of the upper middle back. The upper part of the nodule was covered by a slightly hyperpigmented surface, with numerous small whitish lobules. Microscopic examination revealed a dermatofibroma with sebaceous induction.
Sebaceous induction overlying a dermatofibroma is not frequent, and it occurs in most cases on or near the shoulder. The typical dermoscopic pattern involves many whitish globules or clumps grouped into clusters. The aetiology is unknown but could stem from a conducive microenvironment in shoulder skin, associated with growth factors secreted by the DF.
Accurate Image Search Using the Contextual Dissimilarity Measure Jegou, H.; Schmid, C.; Harzallah, H. ...
IEEE transactions on pattern analysis and machine intelligence,
2010-Jan., 2010, 2010-Jan, 2010-01-00, 20100101, 2010-01, Letnik:
32, Številka:
1
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This paper introduces the contextual dissimilarity measure, which significantly improves the accuracy of bag-of-features-based image search. Our measure takes into account the local distribution of ...the vectors and iteratively estimates distance update terms in the spirit of Sinkhorn's scaling algorithm, thereby modifying the neighborhood structure. Experimental results show that our approach gives significantly better results than a standard distance and outperforms the state of the art in terms of accuracy on the Nisteacuter-Steweacutenius and Lola data sets. This paper also evaluates the impact of a large number of parameters, including the number of descriptors, the clustering method, the visual vocabulary size, and the distance measure. The optimal parameter choice is shown to be quite context-dependent. In particular, using a large number of descriptors is interesting only when using our dissimilarity measure. We have also evaluated two novel variants: multiple assignment and rank aggregation. They are shown to further improve accuracy at the cost of higher memory usage and lower efficiency.
We address the problem of image search on a very large scale, where three constraints have to be considered jointly: the accuracy of the search, its efficiency, and the memory usage of the ...representation. We first propose a simple yet efficient way of aggregating local image descriptors into a vector of limited dimension, which can be viewed as a simplification of the Fisher kernel representation. We then show how to jointly optimize the dimension reduction and the indexing algorithm, so that it best preserves the quality of vector comparison. The evaluation shows that our approach significantly outperforms the state of the art: the search accuracy is comparable to the bag-of-features approach for an image representation that fits in 20 bytes. Searching a 10 million image dataset takes about 50ms.
Aggregating Local Image Descriptors into Compact Codes Jegou, H.; Perronnin, F.; Douze, M. ...
IEEE transactions on pattern analysis and machine intelligence,
09/2012, Letnik:
34, Številka:
9
Journal Article, Conference Proceeding
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This paper addresses the problem of large-scale image search. Three constraints have to be taken into account: search accuracy, efficiency, and memory usage. We first present and evaluate different ...ways of aggregating local image descriptors into a vector and show that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension. We then jointly optimize dimensionality reduction and indexing in order to obtain a precise vector comparison as well as a compact representation. The evaluation shows that the image representation can be reduced to a few dozen bytes while preserving high accuracy. Searching a 100 million image data set takes about 250 ms on one processor core.
The papers in this special section focus on multimedia data retrieval and classification via large-scale systems. Today, large collections of multimedia data are explosively created in different ...fields and have attracted increasing interest in the multimedia research area. Large-scale multimedia data provide great unprecedented opportunities to address many challenging research problems, e.g., enabling generic visual classification to bridge the well-known semantic gap by exploring large-scale data, offering a promising possibility for in-depth multimedia understanding, as well as discerning patterns and making better decisions by analyzing the large pool of data. Therefore, the techniques for large-scale multimedia retrieval, classification, and understanding are highly desired. Simultaneously, the explosion of multimedia data puts urgent needs for more sophisticated and robust models and algorithms to retrieve, classify, and understand these data. Another interesting challenge is, how can the traditional machine learning algorithms be scaled up to millions and even billions of items with thousands of dimensionalities? This motivated the community to design parallel and distributed machine learning platforms, exploiting GPUs as well as developing practical algorithms. Besides, it is also important to exploit the commonalities and differences between different tasks, e.g., image retrieval and classification have much in common while different indexing methods evolve in a mutually supporting way.
Variable length codes (VLCs) exhibit loss of synchronization problems when transmitted over noisy channels. Trellis decoding techniques based on Maximum A Posteriori (MAP) estimators are often used ...to minimize the error rate on the estimated sequence. If the number of symbols and/or bits transmitted is known by the decoder, termination constraints can be incorporated in the decoding process. All the paths in the trellis which do not lead to a valid sequence length are suppressed. This correspondence presents an analytic method to assess the expected error resilience of a VLC when trellis decoding with a sequence length constraint is used. The approach is based on the computation, for a given code, of the amount of information brought by the constraint. It is then shown that this quantity is not significantly altered by appropriate trellis states aggregation. This proves that the performance obtained by running a length-constrained Viterbi decoder on aggregated state models approaches the one obtained with the bit/symbol trellis, with a significantly reduced complexity. It is then shown that the complexity can be further decreased by projecting the state model on two state models of reduced size
Nested melanoma Jegou, M-H; Huet, P; Penchet, I
Annales de dermatologie et de vénéréologie
144, Številka:
1
Journal Article
Recenzirano
Nested melanoma in elderly subjects is an entity that has been reported in the literature only since 2012. In this paper, we describe its distinctive clinical, dermatoscopic and histopathological ...features and compare them to previous published cases, with the aim of highlighting certain specific criteria of this melanoma subtype.
A 52-year-old man was referred for the presence on his chest of a large suspicious pigmented lesion of irregular shape and colour. Dermatoscopically, the lesion was chaotic and characterized by a black, structureless, eccentric area with some peripheral globules as well as some segmental radial lines. Histopathological examination revealed the presence of an asymmetric lesion with large junctional melanocytic nests showing a focal tendency to gathering and some cytological atypia. A diagnosis of nested melanoma was ultimately made.
Nested melanoma of the elderly represents a distinct anatomoclinical variant of superficial spreading melanoma. Clinically, the lesion is usually large and occurs in photodamaged skin. We would stress that the "elderly" criterion is not mandatory given the numerous cases reported in people under 60 years. The main dermatoscopic feature is a globular pattern, but several features characteristic of superficial spreading melanoma may also be present. Histological diagnosis may be difficult because of the mainly nested pattern, and the condition may be confused histologically with a benign junctional nevus. But these large junctional nests of different sizes, with bridging and cytonuclear atypias, together with asymmetry of the lesions are the hallmark of this special kind of melanoma.
Optimum soft decoding of sources compressed with variable length codes and quasi-arithmetic codes, transmitted over noisy channels, can be performed on a bit/symbol trellis. However, the number of ...states of the trellis is a quadratic function of the sequence length leading to a decoding complexity which is not tractable for practical applications. The decoding complexity can be significantly reduced by using an aggregated state model, while still achieving close to optimum performance in terms of bit error rate and frame error rate. However, symbol a posteriori probabilities can not be directly derived on these models and the symbol error rate (SER) may not be minimized. This paper describes a two-step decoding algorithm that achieves close to optimal decoding performance in terms of SER on aggregated state models. A performance and complexity analysis of the proposed algorithm is given.
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