Bloom Filters and Compact Hash Codes for Efficient and Distributed Image Retrieval

This paper presents a novel method for efficient image retrieval, based on a simple and effective hashing of CNN features and the use of an indexing structure based on Bloom filters. These filters are used as gatekeepers for the database of image features, allowing to avoid to perform a query if the query features are not stored in the database and speeding up the query process, without affecting retrieval performance. Thanks to the limited memory requirements the system is suitable for mobile applications and distributed databases, associating each filter to a distributed portion of the database. Experimental validation has been performed on three standard image retrieval datasets, outperforming state-of-the-art hashing methods in terms of precision, while the proposed indexing method obtains a $2\times$ speedup

Near-Duplicate Image Retrieval Based on Contextual Descriptor

The state of the art of technology for near-duplicate image retrieval is mostly based on the Bag-of-Visual-Words model. However, visual words are easy to result in mismatches because of quantization errors of the local features the words represent. In order to improve the precision of visual words matching, contextual descriptors are designed to strengthen their discriminative power and measure the contextual similarity of visual words. This paper presents a new contextual descriptor that measures the contextual similarity of visual words to immediately discard the mismatches and reduce the count of candidate images. The new contextual descriptor encodes the relationships of dominant orientation and spatial position between the referential visual words and their context. Experimental results on benchmark Copydays dataset demonstrate its efficiency and effectiveness for near-duplicate image retrieval

The AXES-lite video search engine

The aim of AXES is to develop tools that provide various types of users with new engaging ways to interact with audiovisual libraries, helping them discover, browse, navigate, search, and enrich archives. This paper describes the initial (lite) version of the AXES search engine, which is targeted at professional users such as media professionals and archivists. We describe the overall system design, the user interface, and the results of our experiments at TRECVid 2011

Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database

This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.Comment: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 24-26 June 201

Named Entity Recognition in Twitter using Images and Text

Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations with correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results. Thus, devising models to deal with such linguistically complex contexts is still challenging. In this paper, we propose a novel multi-level architecture that does not rely on any specific linguistic resource or encoded rule. Unlike traditional approaches, we use features extracted from images and text to classify named entities. Experimental tests against state-of-the-art NER for Twitter on the Ritter dataset present competitive results (0.59 F-measure), indicating that this approach may lead towards better NER models.Comment: The 3rd International Workshop on Natural Language Processing for Informal Text (NLPIT 2017), 8 page