Simulated evaluation of faceted browsing based on feature selection

In this paper we explore the limitations of facet based browsing which uses sub-needs of an information need for querying and organising the search process in video retrieval. The underlying assumption of this approach is that the search effectiveness will be enhanced if such an approach is employed for interactive video retrieval using textual and visual features. We explore the performance bounds of a faceted system by carrying out a simulated user evaluation on TRECVid data sets, and also on the logs of a prior user experiment with the system. We first present a methodology to reduce the dimensionality of features by selecting the most important ones. Then, we discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. Facets created by users are simulated by clustering video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness

Fractional norms and quasinorms do not help to overcome the curse of dimensionality

The curse of dimensionality causes the well-known and widely discussed problems for machine learning methods. There is a hypothesis that using of the Manhattan distance and even fractional quasinorms lp (for p less than 1) can help to overcome the curse of dimensionality in classification problems. In this study, we systematically test this hypothesis. We confirm that fractional quasinorms have a greater relative contrast or coefficient of variation than the Euclidean norm l2, but we also demonstrate that the distance concentration shows qualitatively the same behaviour for all tested norms and quasinorms and the difference between them decays as dimension tends to infinity. Estimation of classification quality for kNN based on different norms and quasinorms shows that a greater relative contrast does not mean better classifier performance and the worst performance for different databases was shown by different norms (quasinorms). A systematic comparison shows that the difference of the performance of kNN based on lp for p=2, 1, and 0.5 is statistically insignificant

Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases

With the proliferation of digital content, efficient image matching in natural image databases has become paramount. Traditional image matching techniques, while effective to a certain extent, face challenges in dealing with the high variability inherent in natural images. This research delves into the application of deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet Networks, to address these challenges. We introduce various techniques to enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, efficient sampling, and the amalgamation of traditional computer vision strategies with deep learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements in image matching efficiency, as quantified by metrics like precision, recall, F1-Score, and matching time. The findings underscore the potential of deep learning as a transformative tool for natural image database matching, setting the stage for further research and optimization in this domain

Boosting multi-kernel Locality-Sensitive Hashing for scalable image retrieval

Ministry of Education, Singapore under its Academic Research Funding Tier