Learning a Complete Image Indexing Pipeline

To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the items relevant to the query; An approximate distance computation mechanism to rapidly scan these lists. While supervised deep learning has recently enabled improvements to the latter, the former continues to be based on unsupervised clustering in the literature. In this work, we propose a first system that learns both components within a unifying neural framework of structured binary encoding

Autophagy inhibition by chloroquine prevents increase in blood pressure and preserves endothelial functions

Purpose: To determine the effects of lysosomal inhibition of autophagy by chloroquine (CHQ) onhypertension-associated changes in the endothelial functions. Method: Angiotensin II (Ang II)-treated human endothelial cell line EA.hy926 and renovascularhypertensive rats were subjected to CHQ treatment (in vitro: 0.5, 1, and 2.5 μM; in vivo: 50 mg/kg/dayfor three weeks). Changes in the protein expressions of LC3b II (autophagosome formation marker) andp62 (autophagy flux marker) were assessed using immunoblotting. Cell migration assay, tubuleformation assay (in vitro), and organ bath studies (in vivo) were performed to evaluate the endothelialfunctions. Hemodynamic parameters were measured as well. Results: A higher expression of LC3b II and a reduced expression of p62 observed in the Ang II-treatedendothelial cells, as well as in the aorta of the hypertensive rats, indicated enhanced autophagy.Treatment with CHQ resulted in reduced autophagy flux (in vitro as well as in vivo) and suppressed AngII-induced endothelial cell migration and angiogenesis (in vitro). The treatment with CHQ was alsoobserved to prevent increase in blood pressure in hypertensive rats and preserved acetylcholineinducedrelaxation in phenylephrine-contracted aorta from the hypertensive rats. In addition, chloroquineattenuated Ang II-induced contractions in the aorta of normotensive as well as hypertensive rats. Conclusion: These observations indicated that CHQ lowers the blood pressure and preserves thevascular endothelial function during hypertension. Keywords: Angiotensin II, Autophagy, Chloroquine, Endothelial function, Hypertension, Vasculardysfunctio

Apprentissage de représentations compactes pour la recherche d'images à grande échelle

This thesis addresses the problem of large-scale image search. To tackle image search at large scale, it is required to encode images with compact representations which can be efficiently employed to compare images meaningfully. Obtaining such compact representation can be done either by compressing effective high dimensional representations or by learning compact representations in an end-to-end manner. The work in this thesis explores and advances in both of these directions. In our first contribution, we extend structured vector quantization approaches such as Product Quantization by proposing a weighted codeword sum representation. We test and verify the benefits of our approach for approximate nearest neighbor search on local and global image features which is an important way to approach large scale image search. Learning compact representation for image search recently got a lot of attention with various deep hashing based approaches being proposed. In such approaches, deep convolutional neural networks are learned to encode images into compact binary codes. In this thesis we propose a deep supervised learning approach for structured binary representation which is a reminiscent of structured vector quantization approaches such as PQ. Our approach benefits from asymmetric search over deep hashing approaches and gives a clear improvement for search accuracy at the same bit-rate. Inverted index is another important part of large scale search system apart from the compact representation. To this end, we extend our ideas for supervised compact representation learning for building inverted indexes. In this work we approach inverted indexing with supervised deep learning and make an attempt to unify the learning of inverted index and compact representation. We thoroughly evaluate all the proposed methods on various publicly available datasets. Our methods either outperform, or are competitive with the state-of-the-art.Cette thèse aborde le problème de la recherche d'images à grande échelle. Pour aborder la recherche d'images à grande échelle, il est nécessaire de coder des images avec des représentations compactes qui peuvent être efficacement utilisées pour comparer des images de manière significative. L'obtention d'une telle représentation compacte peut se faire soit en comprimant des représentations efficaces de grande dimension, soit en apprenant des représentations compactes de bout en bout. Le travail de cette thèse explore et avance dans ces deux directions. Dans notre première contribution, nous étendons les approches de quantification vectorielle structurée telles que la quantification de produit en proposant une représentation somme pondérée de codewords. Nous testons et vérifions les avantages de notre approche pour la recherche approximative du plus proche voisin sur les caractéristiques d'image locales et globales, ce qui est un moyen important d'aborder la recherche d'images à grande échelle. L'apprentissage de la représentation compacte pour la recherche d'images a récemment attiré beaucoup d'attention avec diverses approches basées sur le hachage profond proposées. Dans de telles approches, les réseaux de neurones convolutifs profonds apprennent à coder des images en codes binaires compacts. Dans cette thèse, nous proposons une approche d'apprentissage supervisé profond pour la représentation binaire structurée qui rappelle une approche de quantification vectorielle structurée telle que PQ. Notre approche bénéficie de la recherche asymétrique par rapport aux approches de hachage profond et apporte une nette amélioration de la précision de la recherche au même débit binaire. L'index inversé est une autre partie importante du système de recherche à grande échelle en dehors de la représentation compacte. À cette fin, nous étendons nos idées pour l'apprentissage de la représentation compacte supervisée pour la construction d'index inversés. Dans ce travail, nous abordons l'indexation inversée avec un apprentissage approfondi supervisé et essayons d'unifier l'apprentissage de l'indice inversé et de la représentation compacte. Nous évaluons minutieusement toutes les méthodes proposées sur divers ensembles de données accessibles au public. Nos méthodes surpassent ou sont compétitives avec l'état de l'art