Image Segmentation for Feature Extraction

This chapter explores the prevailing segmentation methods to extract the target object features, in the field of plant pathology for disease diagnosis. The digital images of different plant leaves are taken for analysis as most of the disease symptoms are visible on leaves apart from other vital parts. Among the different phases of processing a digital image, the substantive focus of the study concentrates mainly on the methodology or algorithms deployed on image acquisition, preprocessing, segmentation, and feature extraction. The chapter collects the existing literature survey related to disease diagnosis methods in agricultural plants and prominently highlights the performance of each algorithm by comparing with its counterparts. The main aim is to provide an insight of creativeness to the researchers and experts to develop a less expensive, accurate, fast and an instant system for the timely detection of plant disease, so that appropriate remedial measures can be taken.

Logo Matching and Recognition Based on Context

Logos are graphic productions that recall some real-world objects or emphasize a name, simply display some abstract signs that have strong perceptual appeal. Color may have some relevance to assess the logo identity. Different logos may have a similar layout with slightly different spatial disposition of the graphic elements, localized differences in the orientation, size and shape, or differ by the presence/absence of one or few traits. In this chapter, the author uses ensemble-based framework to choose the best combination of preprocessing methods and candidate extractors. The proposed system has reference logos and test logos which are verified depending on some features like regions, pre-processing, key points. These features are extracted by using gray scale image by scale-invariant feature transform (SIFT) and Affine-SIFT (ASIFT) descriptor method. Pre-processing phase employs four different filters. Key points extraction is carried by SIFT and ASIFT algorithm. Key points are matched to recognize fake logo.

Machine-Learning-Based External Plagiarism Detecting Methodology From Monolingual Documents

In this chapter, an initiative is proposed where natural language processing (NLP) techniques and supervised machine learning algorithms have been combined to detect external plagiarism. The major emphasis is on to construct a framework to detect plagiarism from monolingual texts by implementing n-gram frequency comparison approach. The framework is based on 120 characteristics which have been extracted during pre-processing steps using simple NLP approach. Afterward, filter metrics has been applied to select most relevant features and supervised classification learning algorithm has been used later to classify the documents in four levels of plagiarism. Then, confusion matrix was built to estimate the false positives and false negatives. Finally, the authors have shown C4.5 decision tree-based classifier's suitability on calculating accuracy over naive Bayes. The framework achieved 89% accuracy with low false positive and false negative rate and it shows higher precision and recall value comparing to passage similarities method, sentence similarity method, and search space reduction method.

Dimension Reduction Using Image Transform for Content-Based Feature Extraction

Technological advancements in the field of machine learning have attempted classification of the images of gigantic datasets. Classification with content-based image feature extraction categorizes the images based on the image content in contrast to conventional text-based annotation. The chapter has presented a feature extraction technique based on application of image transform. The method has extracted meaningful features and facilitated feature dimension reduction. A technique, known as fractional coefficient of transforms, is adopted to facilitate feature dimension reduction. Two different color spaces, namely RGB and YUV, are considered to compare the classification metrics to figure out the best possible reduced feature dimension. Further, the results are compared to state-of-the-art techniques which have revealed improved performance for the proposed feature extraction technique.

Detecting and Tracking Segmentation of Moving Objects Using Graph Cut Algorithm

Real-time moving object detection, classification, and tracking capabilities are presented with system operates on both color and gray-scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. Object detection in a video is usually performed by object detectors or background subtraction techniques. The proposed method determines the threshold automatically and dynamically depending on the intensities of the pixels in the current frame. In this method, it updates the background model with learning rate depending on the differences of the pixels in the background model of the previous frame. The graph cut segmentation-based region merging algorithm approaches achieve both segmentation and optical flow computation accurately and they can work in the presence of large camera motion. The algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group, and vehicle.

Segmentation of Multiple Touching Hand Written Devnagari Compound Characters

One of the most widely used steps in the process of reducing images to information is segmentation, which divides the image into regions that hopefully correspond to structural units in the scene or distinguish objects of interest. Segmentation is often described by analogy to visual processes as a foreground/background separation, implying that the selection procedure concentrates on a single kind of feature and discards the rest. Machine-printed or hand-drawn scripts can have various font types or writing styles. The writing styles can be roughly categorized into discrete style (handprint or boxed style), continuous style (cursive style), and mixed style. We can see that the ambiguity of character segmentation has three major sources: (1) variability of character size and inter character space; (2) confusion between inter character and within-character space; and (3) touching between characters.

Clustering Techniques for Content-Based Feature Extraction From Image

Image retrieval is gaining significant attention in areas such as surveillance, access control, etc. The content-based feature extraction plays a very crucial role in image retrieval. For the characterization of a specific image, mainly three features (i.e., color, texture, and shape) are used. Multimedia can store text, image, audio, and video which can be processed and retrieved. The various techniques are used for image retrieval such as textual annotations, content-based image retrieval in many application areas like medical imaging, satellite imaging, etc. However, most of these techniques were designed for specific domains and universally accepted method is yet to be designed; hence, CBIR is a field of active research. Similar output images indicate efficiency of search and retrieval process. In this chapter, the authors have discussed various image feature extraction techniques and clustering approaches for content-based feature extraction from image and specifically focused on color based CBIR techniques.

Fuzzy Techniques for Content-Based Image Retrieval

Content-based image retrieval aims to acquire images from huge databases by analyzing their visual features like color, texture, shape, and spatial relationship. The search for superior accuracy in image retrieval has resulted in concentrating more on semantic gap reduction between the low-level features and high level human reasoning. Fuzzy theory is a prevailing methodology which helps in attaining this goal by using attributes and interpretations similar to human reasoning. The vagueness and impreciseness in image data and the retrieval process can be modeled by fuzzy sets. This chapter analyses fuzzy theoretic approaches in various stages of content-based image retrieval system. Various fuzzy-based feature descriptors are discussed along with different fuzzy classification and indexing algorithms for content-based image retrieval. This chapter also presents an overview of various fuzzy distance and similarity measures for image retrieval. A novel fuzzy theoretic retrieval for finger vein biometric images is also proposed in this chapter with experiment and analysis.

Application of Object Recognition With Shape-Index Identification and 2D Scale Invariant Feature Transform for Key-Point Detection

Humans make object recognition look inconsequential. In this chapter, scale-invariant feature extraction and shape-index depiction are used on a range of images for identifying objects. The shape-index is attained and used as a local descriptor or key-point descriptor. First surface properties for shape index identification and second as 2D scale invariant feature transformed for key-point detection and feature extraction. The object recognition classification is compared results with shape-index identification and 2D scale-invariant feature transform for key-point detection with SIFT and SURF. The authors are using images from the ImageNet dataset, and with use of shift-index + SIFT descriptors, they are finding better accuracy at the classification stage.

An Integrated Framework for Information Identification With Image Data Using Multi-Technique Feature Extraction

Image data has portrayed immense potential as a resourceful foundation of information in current context for numerous applications including biomedicine, military, commerce, education, and web image classification and searching. The scenario has kindled the requirement for efficient content-based image identification from the archived image databases in varied industrial and educational sectors. Feature extraction has acted as the backbone to govern the success rate of content-based information identification with image data. The chapter has presented two different techniques of feature extraction from images based on image binarization and morphological operators. The multi-technique extraction with radically reduced feature size was imperative to explore the rich set of feature content in an image. The objective of this work has been to create a fusion framework for image recognition by means of late fusion with data standardization. The work has implemented a hybrid framework for query classification as a precursor for image retrieval which has been so far the first of its kind.