Novel Architecture for Human Re-Identification with a Two-Stream Neural Network and Attention Mechanism

This paper proposes a novel architecture that utilises an attention mechanism in conjunction with multi-stream convolutional neural networks (CNN) to obtain high accuracy in human re-identification (Reid). The proposed architecture consists of four blocks. First, the pre-processing block prepares the input data and feeds it into a spatial-temporal two-stream CNN (STC) with two fusion points that extract the spatial-temporal features. Next, the spatial-temporal attentional LSTM block (STA) automatically fine-tunes the extracted features and assigns weight to the more critical frames in the video sequence by using an attention mechanism. Extensive experiments on four of the most popular datasets support our architecture. Finally, the results are compared with the state of the art, which shows the superiority of this approach

View-invariant gait person re-identification with spatial and temporal attention

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPerson re-identification at a distance across multiple none overlapping cameras has been an active research area for years. In the past ten years, Short term Person Re-Id techniques have made great strides in terms of accuracy using only appearance features in limited environments. However, massive intraclass variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject appearance and clothing might have changed after a significant period has elapsed. Facing these problems, soft biometric features such as Gait have been proposed in the past. Nevertheless, even Gait can vary with illness, ageing and changes in the emotional state, changes in walking surfaces, shoe type, clothes type, objects carried by the subject and even clutter in the scene. Therefore, Gait is considered a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So, extracting discriminative features from both spatial and temporal domains would be very beneficial to this research. Therefore, this thesis focuses on finding the best and most robust method to tackle the gait human Re-identification problem and solve it for practical applications. In real-world surveillance scenarios, the human gait cycle is primarily abnormal. These abnormalities include but not limited to temporal and spatial characteristics changes such as walking speed, broken gait phase and most importantly, varied camera angles. Our work performed an extensive literature study on spatial and temporal gait feature extraction methods with a focus on deep learning. Next, we conducted a comparative study and proposed a spatial-temporal approach for gait feature extraction using the fusion of multiple modalities, including optical-flow, raw silhouettes and RGB images. This approach was tested on two of the most challenging publicly available datasets for gait recognition TUM-GAID and CASIA-B, with excellent results presented in chapter 3. Furthermore, a modern spatial-temporal attention mechanism was proposed and tested on CASIA-B and OULP datasets which learns salient features independent of the gait cycle and view variations. The spatial attention layer in the proposed method extracts the spatial feature maps using a two-layered architecture that are fused using late fusion. It can pay attention to the identity-related salient regions in silhouette sequences discriminatively using the spatial feature maps. The temporal attention layer consists of an LSTM that encodes the temporal motion for silhouette sequences. It uses the encoded output vectors in the temporal attention architecture to focus on the most critical timesteps in the gait cycle and discard the rest. Furthermore, we improved the performance of our method by mapping our extracted spatial-temporal gait features to a discriminative null space for use in our Siamese architecture for crossmatching. We also conducted an element removal experiment on each segment of our spatial-temporal attentional network to gain insight into each component’s contribution to the performance. Our method showed outstanding robustness against abnormal gait cycles as well as viewpoint variations on both benchmark datasets

GPNMB methylation: a new marker of potentially carcinogenic colon lesions

Abstract Background Epigenetic plays an important role in colorectal neoplasia process. There is a need to determine sound biomarkers of colorectal cancer (CRC) progression with clinical and therapeutic implications. Therefore, we aimed to examine the role and methylation status of Glyco Protein Non-Metastatic GPNM B (GPNMB) gene in normal, adenoma and CRC in African American (AA) patients. Methods The methylation status of 13 CpG sites (chr7: 23287345–23,287,426) in GPNMB gene’s promoter, was analyzed by pyrosequencing in human CRC cell lines (HCT116, SW480, and HT29) and microdissected African American paraffin embedded samples (20 normal, 21 non-advanced adenoma (NA), 48 advanced adenoma (AD), and 20 cancer tissues. GPNMB expression was analyzed by immunohistochemistry (IHC) on tissue microarrays (TMA). Correlations between GPNMB methylation and expression with clinicopathological features were analyzed. GPNMB functional analysis was performed in triplicates using cell proliferation, migration and invasion assays in HCT116 colon cell line after stable transfection with a GPNMB-cDNA expression vector. Results GPNMB methylation was lower in normal mucosa compared to CRC samples (1/20 [5%] vs. 18/20 [90%]; P  0.05) compared to the mock-transfected cells. Conclusion Our data indicate a high methylation profile leading to a lower GPNMB expression in adenoma and CRC samples. The functional analysis established GPNMB as a potential tumor suppressor gene. As such, GPNMB might be useful as a biomarker of adenomas with high carcinogenic potential

Identification of novel mutations by exome sequencing in African American colorectal cancer patients.

<p>BACKGROUND: The purpose of this study was to identify genome-wide single nucleotide variants and mutations in African American patients with colorectal cancer (CRC). There is a need of such studies in African Americans, because they display a higher incidence of aggressive CRC tumors.</p> <p>METHODS: We performed whole exome sequencing (WES) on DNA from 12 normal/tumor pairs of African American CRC patient tissues. Data analysis was performed using the software package GATK (Genome Analysis Tool Kit). Normative population databases (eg, 1000 Genomes SNP database, dbSNP, and HapMap) were used for comparison. Variants were annotated using analysis of variance and were validated via Sanger sequencing.</p> <p>RESULTS: We identified somatic mutations in genes that are known targets in CRC such as APC, BRAF, KRAS, and PIK3CA. We detected novel alterations in the Wnt pathway gene, APC, within its exon 15, of which mutations are highly associated with CRC.</p> <p>CONCLUSIONS: This WES study in African American patients with CRC provides insight into the identification of novel somatic mutations in APC. Our data suggest an association between specific mutations in the Wnt signaling pathway and an increased risk of CRC. The analysis of the pathogenicity of these novel variants may shed light on the aggressive nature of CRC in African Americans. Cancer 2014. © 2014 American Cancer Society.</p