Pierre Tirilly

Evaluating users’ satisfaction in packet networks using random neural networks

Quantifying the quality of a video or audio transmission over the Internet is usually a hard task, as based on the statistical processing of the evaluations made by a panel of humans (the corresponding and standardized area is called subjective testing). In this paper we describe a methodology called Pseudo-Subjective Quality Assessment (PSQA), based on Random Neural Networks, which is able to perform this task automatically, accurately and efficiently. RNN had been chosen here because of their good performances over other possibilities; this is discussed in the paper. Some new insights on PSQA’s use and performance are also given. In particular we discuss new results concerning PSQA–based dynamic quality control, and conversational quality assessment.

Distances and weighting schemes for bag of visual words image retrieval

Current text retrieval techniques allow to index and retrieve text documents very efficiently and with a good accuracy. Image retrieval, on the contrary, is still very coarse and does not yield satisfying results. Therefore, computer vision researchers try to benefit from text retrieval contributions to enhance their retrieval systems. In particular, Sivic and Zisserman, with their video-google framework [1], propose a description of images similar to standard text descriptors: images are described by elementary image parts, called visual words. Thus, they perform image retrieval using the standard Vector Space Model (VSM) of Information Retrieval (IR) and benefit from some classical IR techniques such as inverted files. Among available text retrieval techniques, automatically finding the most relevant words to describe a document has been intensively studied for texts, but not for images. In this paper, we propose to explore the use of term weighting techniques and classical distances from text retrieval in the case of images. These weights are standard VSM weights, weights derived from probabilistic models of IR or new weighting schemes that we propose. Our experiments, performed on several datasets, show that no weighting scheme can improve retrieval on every dataset, but also that choosing weights fitting the properties of the dataset can improve precision and MAP up to 10%. This study provides some interesting insights about the semantic and statistical differences between textual and visual words, and about the way visual word-based image retrieval systems can be optimized. It also shows some limits of the bag of visual words model, and the relation existing between Minkowski distances and local weighting. At last, this study questions some experimental habits commonly found in the literature (choice of L1 distance, TF*IDF weights and evaluation using one dataset only).

On modality classification and its use in text-based image retrieval in medical databases

Medical databases have been a popular application field for image retrieval techniques during the last decade. More recently, much attention has been paid to the prediction of medical image modality (X-rays, MRI…) and the integration of the predicted modality into image retrieval systems. This paper addresses these two issues. On the one hand, we believe it is possible to design specific visual descriptors to determine image modality much more efficiently than the traditional image descriptors currently used for this task. We propose very light image descriptors that better describe the modality properties and show promising results. On the other hand, we present a comparison of different existing or new modality integration methods. This comprehensive study provide insights on the behavior of these models with respect to the initial classification and retrieval systems. These results can be extended to other applications with a similar framework. All the experiments presented in this work are performed using datasets provided during the 2009 and 2010 ImageCLEF medical tracks.

Constructing a true LCSH tree of a science and engineering collection

The Library of Congress Subject Headings (LCSH) is a subject structure used to index large library collections throughout the world. Browsing a collection through LCSH is difficult using current online tools in part because users cannot explore the structure using their existing experience navigating file hierarchies on their hard drives. This is due to inconsistencies in the LCSH structure, which does not adhere to the specific rules defining tree structures. This article proposes a method to adapt the LCSH structure to reflect a real-world collection from the domain of science and engineering. This structure is transformed into a valid tree structure using an automatic process. The analysis of the resulting LCSH tree shows a large and complex structure. The analysis of the distribution of information within the LCSH tree reveals a power law distribution where the vast majority of subjects contain few information items and a few subjects contain the vast majority of the collection.

Reducing Subject Tree Browsing Complexity

Many large digital collections are currently organized by subject; although useful, these information organization structures are large and complex and thus difficult to browse. Current online tools and visualization prototypes show small, localized subsets and do not provide the ability to explore the predominant patterns of the overall subject structure. This study describes subject tree modifications that facilitate browsing for documents by capitalizing on the highly uneven distribution of real-world collections. The approach is demonstrated on two large collections organized by the Library of Congress Subject Headings (LCSH) and Medical Subject Headings (MeSH). Results show that the LCSH subject tree can be reduced to 49% of its initial complexity while maintaining access to 83% of the collection, and the MeSH tree can be reduced to 45% of its initial complexity while maintaining access to 97% of the collection. A simple solution to negate the loss of access is discussed. The visual impact is demonstrated by using traditional outline views and a slider control allowing searchers to change the subject structure dynamically according to their needs. This study has implications for the development of information organization theory and human–information interaction techniques for subject trees.

Exploiting major trends in subject hierarchies for large-scale collection visualization

Many large digital collections are currently organized by subject; however, these useful information organization structures are large and complex, making them difficult to browse. Current online tools and visualization prototypes show small localized subsets and do not provide the ability to explore the predominant patterns of the overall subject structure. This research addresses this issue by simplifying the subject structure using two techniques based on the highly uneven distribution of real-world collections: level compression and child pruning. The approach is demonstrated using a sample of 130K records organized by the Library of Congress Subject Headings (LCSH). Promising results show that the subject hierarchy can be reduced down to 42% of its initial size, while maintaining access to 81% of the collection. The visual impact is demonstrated using a traditional outline view allowing searchers to dynamically change the amount of complexity that they feel necessary for the tasks at hand.

Image similarity as assessed by users: A quantitative study

Image retrieval systems are generally based on the notion of image similarity: they compute similarity scores between the images of the database and a query (image or text), and organize the images according to these scores. However, this notion is ill-defined, and the collections used to train and evaluate image retrieval systems are based on similarity judgments that rely on simplistic, non-realistic, assumptions. This paper addresses the issue of the definition of image similarity, and more precisely the two following questions: do humans assess image similarity in the same way? Is it possible to define reference similarity judgments that would correspond to the perception of most users? An experiment is proposed, in which human subjects are assigned two tasks that fall in principle to the system: rating the similarity of images and ranking images according to a reference image. The data provided by the subjects is analyzed quantitatively to the light of the two aforementioned questions. Results show that the subjects do not have collective strategies of similarity assessment, but that a satisfying consensus can be found individually on the data samples used in the experiments. Based on this, methods to define reference similarity scores and rankings are proposed, that can be used on a larger scale to produce realistic ground truths for the evaluation of image retrieval systems. This study is a first step towards a general, realistic, definition of the notion of image similarity in the context of image retrieval.

Towards the improvement of textual anatomy image classification using image local features

Image classification methods based on text utilize terms extracted from image annotations (image caption, image-related article, etc.) to achieve classification. For images involving different anatomical structures (chest, spine, etc.), however, the precision of pure textual classification often suffers from highly complex text contents (e.g. text terms extracted out of two MR abdomen images may be quite different from each other: terms from one image may concerns gastroenteritis while the other contains terms involving hysteromyoma). This paper tackles the anatomy image classification problem using a hybrid approach. First, a mutual information (MI) based filter is applied to select a set of terms with top MI scores for each anatomical class and help reduce the noise existing in the raw text. Second, local features extracted from the images are transformed as visual descriptors. Last, a hybrid scheme on the results from the textual and visual methods is applied to achieved further improvement of the classification results. Experiments show that this hybrid scheme improves the results over the sole textual or visual method on different anatomical class settings.

On the consistency and features of image similarity

Image indexing and retrieval systems mostly rely on the computation of similarity measures between images. This notion is ill-defined, generally based on simplistic assumptions that do not fit the actual context of use of image retrieval systems. This paper addresses two fundamental issues related to image similarity: checking whether the degree of similarity between two images is perceived consistently by different users and establishing the elements of the images on which users base their similarity judgment. A study is set up, in which human subjects have been asked to assess the degree of the pairwise similarity of images and describe the features on which they base their judgments. The quantitative analysis of the similarity scores reported by the subjects shows that users reach a certain consensus on similarity assessment. From the qualitative analysis of the transcripts of the records of the experiments, a list of the features used by the subjects to assess image similarity is built. From this, a new model of image description emerges. As compared to existing models, it is more realistic, free of preconceptions and more suited to the task of similarity computation. These results are discussed from the perspectives of psychology and computer science.

Introducing FoxPersonTracks: A benchmark for person re-identification from TV broadcast shows

This paper introduces a novel person track dataset dedicated to person re-identification. The dataset is built from a set of real life TV shows broadcasted from BFMTV and LCP TV French channels, provided during the REPERE challenge. It contains a total of 4,604 persontracks (short video sequences featuring an individual with no background) from 266 persons. The dataset has been built from the REPERE dataset by following several automated processing and manual selection/filtering steps. It is meant to serve as a benchmark in person re-identification from images/videos. The dataset also provides re-identifications results using space-time histograms as a baseline, together with an evaluation tool in order to ease the comparison to other re-identification methods.