In this paper, we propose and analyze Vec2Doc, a novel training-free method to transform dense vectors into sparse integer vectors, facilitating the use of inverted indexes for information retrieval (IR). The exponential growth of deep learning and artificial intelligence has revolutionized scientific problem-solving in areas such as computer vision, natural language processing, and automatic content generation. These advances have also significantly impacted IR, with a better understanding of natural language and multimodal content analysis leading to more accurate information retrieval. Despite these developments, modern IR relies primarily on the similarity evaluation of dense vectors from the latent spaces of deep neural networks. This dependence introduces substantial challenges in performing similarity searches on large collections containing billions of vectors. Traditional IR methods, which employ inverted indexes and vector space models, are adept at handling sparse vectors but do not work well with dense ones. Vec2Doc attempts to fill this gap by converting dense vectors into a format compatible with conventional inverted index techniques. Our preliminary experimental evaluations show that Vec2Doc is a promising solution to overcome the scalability problems inherent in vector-based IR, offering an alternative method for efficient and accurate large-scale information retrieval.

VISIONE is a large-scale video retrieval system that integrates multiple search functionalities, including free text search, spatial color and object search, visual and semantic similarity search, and temporal search. The system leverages cutting-edge AI technology for visual analysis and advanced indexing techniques to ensure scalability. As demonstrated by its runner-up position in the 2023 Video Browser Showdown competition, VISIONE effectively integrates these capabilities to provide a comprehensive video retrieval solution. A system demo is available online, showcasing its capabilities on over 2300 hours of diverse video content (V3C1+V3C2 dataset) and 12 hours of highly redundant content (Marine dataset). The demo can be accessed at https://visione.isti.cnr.it/.

Approximate Nearest Neighbor (ANN) search is a prevalent paradigm for searching intrinsically high dimensional objects in large-scale data sets. Recently, the permutation-based approach for ANN has attracted a lot of interest due to its versatility in being used in the more general class of metric spaces. In this approach, the entire database is ranked by a permutation distance to the query. Typically, permutations allow the efficient selection of a candidate set of results, but typically to achieve high recall or precision this set has to be reviewed using the original metric and data. This can lead to a sizeable percentage of the database being recalled, along with many expensive distance calculations. To reduce the number of metric computations and the number of database elements accessed, we propose here a re-ranking based on a local embedding using the nSimplex projection. The nSimplex projection produces Euclidean vectors from objects in metric spaces which possess the n-point property. The mapping is obtained from the distances to a set of reference objects, and the original metric can be lower bounded and upper bounded by the Euclidean distance of objects sharing the same set of references. Our approach is particularly advantageous for extensive databases or expensive metric function. We reuse the distances computed in the permutations in the first stage, and hence the memory footprint of the index is not increased. An extensive experimental evaluation of our approach is presented, demonstrating excellent results even on a set of hundreds of millions of objects.

In the last years, content-based image retrieval largely benefited from representation extracted from deeper and more complex convolutional neural networks, which became more effective but also more computationally demanding. Despite existing hardware acceleration, query processing times may be easily saturated by deep feature extraction in high-throughput or real-time embedded scenarios, and usually, a trade-off between efficiency and effectiveness has to be accepted. In this work, we experiment with the recently proposed continuous neural networks defined by parametric ordinary differential equations, dubbed ODE-Nets, for adaptive extraction of image representations. Given the continuous evolution of the network hidden state, we propose to approximate the exact feature extraction by taking a previous "near-in-time" hidden state as features with a reduced computational cost. To understand the potential and the limits of this approach, we also evaluate an ODE-only architecture in which we minimize the number of classical layers in order to delegate most of the representation learning process --- and thus the feature extraction process --- to the continuous part of the model. Preliminary experiments on standard benchmarks show that we are able to dynamically control the trade-off between efficiency and effectiveness of feature extraction at inference-time by controlling the evolution of the continuous hidden state. Although ODE-only networks provide the best fine-grained control on the effectiveness-efficiency trade-off, we observed that mixed architectures perform better or comparably to standard residual nets in both the image classification and retrieval setups while using fewer parameters and retaining the controllability of the trade-off.

Content-Based Image Retrieval in large archives through the use of visual features has become a very attractive research topic in recent years. The cause of this strong impulse in this area of research is certainly to be attributed to the use of Convolutional Neural Network (CNN) activations as features and their outstanding performance. However, practically all the available image retrieval systems are implemented in main memory, limiting their applicability and preventing their usage in big-data applications. In this paper, we propose to transform CNN features into textual representations and index them with the well-known full-text retrieval engine Elasticsearch. We validate our approach on a novel CNN feature, namely Regional Maximum Activations of Convolutions. A preliminary experimental evaluation, conducted on the standard benchmark INRIA Holidays, shows the effectiveness and efficiency of the proposed approach and how it compares to state-of-the-art main-memory indexes.

In this paper, we adapt a surrogate text representation technique to develop efficient instance-level image retrieval using Regional Maximum Activations of Convolutions (R-MAC). R-MAC features have recently showed outstanding performance in visual instance retrieval. However, contrary to the activations of hidden layers adopting ReLU (Rectified Linear Unit), these features are dense. This constitutes an obstacle to the direct use of inverted indexes, which rely on sparsity of data. We propose the use of deep permutations, a recent approach for efficient evaluation of permutations, to generate surrogate text representation of R-MAC features, enabling indexing of visual features as text into a standard search-engine. The experiments, conducted on Lucene, show the effectiveness and efficiency of the proposed approach.

Vector of locally aggregated descriptors (VLAD) is a promising approach for addressing the problem of image search on a very large scale. This representation is proposed to overcome the quantization error problem faced in Bag-of-Words (BoW) representation. In this paper, we propose to enable inverted files of standard text search engines to exploit VLAD representation to deal with large-scale image search scenarios. We show that the use of inverted files with VLAD significantly outperforms BoW in terms of efficiency and effectiveness on the same hardware and software infrastructure.

Applications of image content recognition, as for instance landmark recognition, can be obtained by using techniques of kNN classifications based on the use of local image features, such as SIFT or SURF. Quality of image classification can be improved by defining geometric consistency check rules based on space transformations of the scene depicted in images. However, this prevents the use of state of the art access methods for similarity searching and sequential scan of the images in the training sets has to be executed in order to perform classification. In this paper we propose a technique that allows one to use access methods for similarity searching, such as those exploiting metric space properties, in order to perform kNN classification with geometric consistency checks. We will see that the proposed approach, in addition to offer an obvious efficiency improvement, surprisingly offers also an improvement of the effectiveness of the classification.

While triggers have become a classical ingredient of relational database systems, research in active databases is aiming at extending the functionality and expressive power of active rules beyond the scope of relational triggers. One of the most important current trend concerns the support of detached active rules, i.e., of rules which are executed as separate transactions, running outside of the scope of the transaction which generates the triggering event. Detached rules have important applications in workflow management and global integrity maintenance across transactions. One of the main issues in designing the rule engine for detached rules is determining their optimal scheduling. In this paper, we study the performance of a detached rule scheduler whose objective is to minimize the interference of detached rule execution with regard to the normal transactional load. This objective is achieved by executing detached rules at given periods of time and by assigning them a fixed amount of dedicated threads; we study the performance of the scheduler relative to the two most critical design parameters, the frequency of execution of the scheduler, and the number of dedicated execution threads.