REINA at CLEF 2007 Robust Task

Describe el trabajo que el Grupo de Investigación REINA de la Universidad de Salamanca presentó en CLEF 2007 sobre sistemas de recuperación de información monolingüe (Inglés, Francés y Portugués) y bilingüe (Inglés al Francés). Muestra los resultados obtenidos en las evaluaciones aplicando técnicas de expansión local.This paper describes our work at CLEF 2007 Robust Task. We have participated in the monolingual (English, French and Portuguese) and the bilingual (English to French) subtask. At CLEF 2006 our research group obtained very good results applying local query expansion using windows of terms in the robust task. This year we have used the same expansion technique, but taking into account some criteria of robustness: MAP, GMAP, MMR, GS@10, P@10, number of failed topics, number of topics bellow 0.1 MAP, and number of topics with P@10=0. In bilingual retrieval experiments threemachine translation programs were used to translate topics. For the target language, translations were merged before performing a monolingual retrieval. We also applied the same local expansion technique. This year the results were disappointing. We think out that the reason is the difficulty to select the best measurement for robustness. Perhaps the problem is that all measurements are average results over all topics, but thehard topics are inherently hard and must be analyze separately. This year all our runs also ends up in good ranking, both base runs and expanded ones. We think that the reason is that we used a good information retrieval system, and the expansion technique is robust because it does not deteriorate significantly the retrieval performance

Particular object retrieval with integral max-pooling of CNN activations

Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image re-ranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets

Multi-scale Orderless Pooling of Deep Convolutional Activation Features

Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective scheme called multi-scale orderless pooling (MOP-CNN). This scheme extracts CNN activations for local patches at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result. The resulting MOP-CNN representation can be used as a generic feature for either supervised or unsupervised recognition tasks, from image classification to instance-level retrieval; it consistently outperforms global CNN activations without requiring any joint training of prediction layers for a particular target dataset. In absolute terms, it achieves state-of-the-art results on the challenging SUN397 and MIT Indoor Scenes classification datasets, and competitive results on ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets

Interoperability between Multimedia Collections for Content and Metadata-Based Searching

Artiste is a European project developing a cross-collection search system for art galleries and museums. It combines image content retrieval with text based retrieval and uses RDF mappings in order to integrate diverse databases. The test sites of the Louvre, Victoria and Albert Museum, Uffizi Gallery and National Gallery London provide their own database schema for existing metadata, avoiding the need for migration to a common schema. The system will accept a query based on one museum’s fields and convert them, through an RDF mapping into a form suitable for querying the other collections. The nature of some of the image processing algorithms means that the system can be slow for some computations, so the system is session-based to allow the user to return to the results later. The system has been built within a J2EE/EJB framework, using the Jboss Enterprise Application Server

Overview of the 2005 cross-language image retrieval track (ImageCLEF)

The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content-based retrieval methods for cross-language image retrieval. Four tasks were offered in the ImageCLEF track: a ad-hoc retrieval from an historic photographic collection, ad-hoc retrieval from a medical collection, an automatic image annotation task, and a user-centered (interactive) evaluation task that is explained in the iCLEF summary. 24 research groups from a variety of backgrounds and nationalities (14 countries) participated in ImageCLEF. In this paper we describe the ImageCLEF tasks, submissions from participating groups and summarise the main fndings

A Technology Proposal for a Management Information System for the Director’s Office, NAL.

This technology proposal attempts in giving a viable solution for a Management Information System (MIS) for the Director's Office. In today's IT scenario, an Organization's success greatly depends on its ability to get accurate and timely data on its operations of varied nature and to manage this data effectively to guide its activities and meet its goals. To cater to the information needs of an Organization or an Office like the Director's Office, information systems are developed and deployed to gather and process data in ways that produce a variety of information to the end-user. MIS can therefore can be defined as an integrated user-machine system for providing information to support operations, management and decision-making functions in an Organization. The system in a nutshell, utilizes computer hardware and software, manual procedures, models for analysis planning, control and decision-making and a database. Using state-of-the-art front-end and back-end web based tools, this technology proposal attempts to provide a single-point Information Management, Information Storage, Information Querying and Information Retrieval interface to the Director and his office for handling all information traffic flow in and out of the Director's Office