Automatic plankton quantification using deep features

The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with handdesigned local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using Convolutional Neural Networks (CNNs) trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms

Improved White Blood Cells Classification based on Pre-trained Deep Learning Models

Leukocytes, or white blood cells (WBCs), are microscopic organisms that fight against infectious disease, bacteria, viruses, and others. The manual method to classify and count WBCs is tedious, time-consuming and may has inaccurate results, whereas the automated methods are costly. The objective of this work is to automatically identify and classify WBCs in a microscopic image into four types with higher accuracy. BCCD is the used dataset in this study, which is a scaled down blood cell detection dataset. BCCD is firstly pre-processed by passing through several processes such as segmentation and augmentation,then it is passed to the proposed model. Our model combines the privilege of deep models in automatically extracting features with the higher classification accuracy of traditional machine learning classifiers.The proposed model consists of two main layers; a shallow tuning pre-trained model and a traditional machine learning classifier on top of it. Here, ten different pretrained models with six different machine learning are used in this study. Moreover, the fully connected network (FCN) of pretrained models is used as a baseline classifier for comparison. The evaluation process shows that the hybrid between MobileNet-224 as feature extractor with logistic regression as classifier has a higher rank-1 accuracy with 97.03%. Besides, the proposed hybrid model outperformed the baseline FCN with 25.78% on average

Enhancing Automatic Annotation for Optimal Image Retrieval

Image search and retrieval based on content is very cumbersome task particularly when the image database is large. The accuracy of the retrieval as well as the processing speed are two important measures used for assessing and comparing the effectiveness of various systems. Text retrieval is more mature and advanced than image content retrieval. In this dissertation, the focus is on converting image content into text tags that can be easily searched using standard search engines where the size and speed issues of the database have been already dealt with. Therefore, image tagging becomes an essential tool for image retrieval from large image databases. Automation of image tagging has received considerable attention by many researchers in recent years. The optimal goal of image description is to automatically annotate images with tags that semantically represent the image content. The speed and accuracy of Image retrieval from large databases are few of the important domains that can benefit from automatic tagging. In this work, several state of the art image classification and image tagging techniques are reviewed. We propose a new self-learning multilayered tagging framework that can address the limitations of current approaches and provide mutual accuracy improvement between the recognition layer and the annotation layer. Our results indicate that the proposed framework can improve the overall accuracy of information retrieval in a variety of image databases

Concurrent evolution of feature extractors and modular artificial neural networks

Artificial Neural Networks (ANNs) are commonly used in both academia and industry as a solution to challenges in the pattern recognition domain. However, there are two problems that must be addressed before an ANN can be successfully applied to a given recognition task: ANN customization and data pre-processing. First, ANNs require customization for each specific application. Although the underlying mathematics of ANNs is well understood, customization based on theoretical analysis is impractical because of the complex interrelationship between ANN behavior and the problem domain. On the other hand, an empirical approach to the task of customization can be successful with the selection of an appropriate test domain. However, this latter approach is computationally intensive, especially due to the many variables that can be adjusted within the system. Additionally, it is subject to the limitations of the selected search algorithm used to find the optimal solution. Second, data pre-processing (feature extraction) is almost always necessary in order to organize and minimize the input data, thereby optimizing ANN performance. Not only is it difficult to know what and how many features to extract from the data, but it is also challenging to find the right balance between the computational requirements for the preprocessing algorithm versus the ANN itself. Furthermore, the task of developing an appropriate pre-processing algorithm usually requires expert knowledge of the problem domain, which may not always be available. This paper contends that the concurrent evolution of ANNs and data pre-processors allows the design of highly accurate recognition networks without the need for expert knowledge in the application domain. To this end, a novel method for evolving customized ANNs with correlated feature extractors was designed and tested. This method involves the use of concurrent evolutionary processes (CEPs) as a mechanism to search the space of recognition networks. In a series of controlled experiments the CEP was applied to the digit recognition domain to show that the efficacy of this method is in-line with results seen in other digit recognition research, but without the need for expert knowledge in image processing techniques for digit recognition