Adaptive weights learning in CNN feature fusion for crime scene investigation image classification

The combination of features from the convolutional layer and the fully connected layer of a convolutional neural network (CNN) provides an effective way to improve the performance of crime scene investigation (CSI) image classification. However, in existing work, as the weights in feature fusion do not change after the training phase, it may produce inaccurate image features which affect classification results. To solve this problem, this paper proposes an adaptive feature fusion method based on an auto-encoder to improve classification accuracy. The method includes the following steps: Firstly, the CNN model is trained by transfer learning. Next, the features of the convolution layer and the fully connected layer are extracted respectively. These extracted features are then passed into the auto-encoder for further learning with Softmax normalisation to obtain the adaptive weights for performing final classification. Experiments demonstrated that the proposed method achieves higher CSI image classification performance compared with fix weights feature fusion. © 2021 Informa UK Limited, trading as Taylor & Francis Group

The Design of a Multimedia-Forensic Analysis Tool (M-FAT)

Digital forensics has become a fundamental requirement for law enforcement due to the growing volume of cyber and computer-assisted crime. Whilst existing commercial tools have traditionally focused upon string-based analyses (e.g., regular expressions, keywords), less effort has been placed towards the development of multimedia-based analyses. Within the research community, more focus has been attributed to the analysis of multimedia content; they tend to focus upon highly specialised specific scenarios such as tattoo identification, number plate recognition, suspect face recognition and manual annotation of images. Given the everincreasing volume of multimedia content, it is essential that a holistic Multimedia-Forensic Analysis Tool (M-FAT) is developed to extract, index, analyse the recovered images and provide an investigator with an environment with which to ask more abstract and cognitively challenging questions of the data. This paper proposes such a system, focusing upon a combination of object and facial recognition to provide a robust system. This system will enable investigators to perform a variety of forensic analyses that aid in reducing the time, effort and cognitive load being placed on the investigator to identify relevant evidence

AN OBJECT-BASED MULTIMEDIA FORENSIC ANALYSIS TOOL

With the enormous increase in the use and volume of photographs and videos, multimedia-based digital evidence now plays an increasingly fundamental role in criminal investigations. However, with the increase, it is becoming time-consuming and costly for investigators to analyse content manually. Within the research community, focus on multimedia content has tended to be on highly specialised scenarios such as tattoo identification, number plate recognition, and child exploitation. An investigator’s ability to search multimedia data based on keywords (an approach that already exists within forensic tools for character-based evidence) could provide a simple and effective approach for identifying relevant imagery. This thesis proposes and demonstrates the value of using a multi-algorithmic approach via fusion to achieve the best image annotation performance. The results show that from existing systems, the highest average recall was achieved by Imagga with 53% while the proposed multi-algorithmic system achieved 77% across the select datasets. Subsequently, a novel Object-based Multimedia Forensic Analysis Tool (OM-FAT) architecture was proposed. The OM-FAT automates the identification and extraction of annotation-based evidence from multimedia content. Besides making multimedia data searchable, the OM-FAT system enables investigators to perform various forensic analyses (search using annotations, metadata, object matching, text similarity and geo-tracking) to help investigators understand the relationship between artefacts, thus reducing the time taken to perform an investigation and the investigator’s cognitive load. It will enable investigators to ask higher-level and more abstract questions of the data, then find answers to the essential questions in the investigation: what, who, why, how, when, and where. The research includes a detailed illustration of the architectural requirements, engines, and complete design of the system workflow, which represents a full case management system. To highlight the ease of use and demonstrate the system’s ability to correlate between multimedia, a prototype was developed. The prototype integrates the functionalities of the OM-FAT tool and demonstrates how the system would help digital investigators find pieces of evidence among a large number of images starting from the acquisition stage and ending in the reporting stage with less effort and in less time.The Higher Committee for Education Development in Iraq (HCED

Iconic Indexing for Video Search

Submitted for the degree of Doctor of Philosophy, Queen Mary, University of London

A review of content-based video retrieval techniques for person identification

The rise of technology spurs the advancement in the surveillance field. Many commercial spaces reduced the patrol guard in favor of Closed-Circuit Television (CCTV) installation and even some countries already used surveillance drone which has greater mobility. In recent years, the CCTV Footage have also been used for crime investigation by law enforcement such as in Boston Bombing 2013 incident. However, this led us into producing huge unmanageable footage collection, the common issue of Big Data era. While there is more information to identify a potential suspect, the massive size of data needed to go over manually is a very laborious task. Therefore, some researchers proposed using Content-Based Video Retrieval (CBVR) method to enable to query a specific feature of an object or a human. Due to the limitations like visibility and quality of video footage, only certain features are selected for recognition based on Chicago Police Department guidelines. This paper presents the comprehensive reviews on CBVR techniques used for clothing, gender and ethnic recognition of the person of interest and how can it be applied in crime investigation. From the findings, the three recognition types can be combined to create a Content-Based Video Retrieval system for person identification

How Scopus is Shaping the Research Publications of Feature Fusion-Based Image Retrieval

Research trends have shown an increase in the preferences for feature fusion-based image retrieval. The primary objective of this study is to show the current state of research regarding image retrieval and feature fusion. The research papers indexed in the Scopus database are considered here for quantitative analysis. A bibliometric analysis of the research publications indexed in Scopus is presented in this study. During this study, 461 documents from 276 different sources are obtained. The important keywords, sources, authors, countries, and funding agencies are presented, which will help future researchers in research directions