Indian River Watershed Image Analysis Using Fuzzy-CA Hybrid Approach

Image segmentation among overlapping land cover areas in satellite images is a very crucial task. Detection of belongingness is the important problem for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of Fuzzy C-Means and Cellular automata methods. This new unsupervised method is able to detect clusters using 2-Dimensional Cellular Automata model based on fuzzy segmentations. This approach detects the overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase of our method, we utilize a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. We experiment our method on Indian Ajoy river watershed area. The clustered regions are compared with well-known FCM and K-Means methods and also with the ground truth knowledge. The results show the superiority of our new method.

Time-Slot Based Intelligent Music Recommender in Indian Music

Music listening is one of the most common thing of human behaviors. Normally mobile music is downloaded to mobile phones and played by mobile phones. Today millennial people use mobile music in about all the age groups. Music recommendation system enhances personalized music classifications that create a profile with the service and build up a music library based on the choice preferences using mobile cloud services. Music recommendation through cloud is therefore an emerging field, and this can be done using various parameters like song genre similarity, human behavior, human mood, song rhythmic patterns, seasons etc. In this article an intelligent music recommender system that identifies the raga name of one particular song music and then mapping with the raga time database and classify the songs according to their playing time and create time slot based personalized music libraries.

Data Compression as a Base for eHealth Interoperability

eHealth is a set of systems and services that enable the sharing of medical diagnostic imaging data remotely. The application of eHealth solves the problem of the lack of specialized personnel, unnecessary execution of multiple diagnostic imaging and rapid exchange of information and remote diagnostics. Medical imaging generates large amounts of data. An MRI study can contain up to several Gigabytes (GB). The exchange of such large amounts of data in the local network facilities is a significant problem due to bandwidth sharing which is even more significant in mobile and wireless networks. A possible solution to this problem is data compression with the requirement that there is no loss of data. The goal of this chapter is a conceptual compression prototype that will allow faster and more efficient exchange of medical images in systems with limited bandwidth and communication speeds (cellular networks, wireless networks). To obtain this conceptual compression prototype we will use wavelets.

Retrieval of Multimedia Information Using Content-Based Image Retrieval (CBIR) Techniques

This chapter will focus on the concept of Content-based image retrieval. Searching of an image or video database based on text based description is a manual labor intensive process. Descriptions of the file are usually typed manually for each image by human operators because the automatic generation of keywords for the images is difficult without incorporation of visual information and feature extraction. This method is impractical in today's multimedia information era. “Content-based” means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and descriptions associated with the image. The term “content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Several important sections are highlighted in this chapter, like architectures, query techniques, multidimensional indexing, video retrieval and different application sections of CBIR.

Content Coverage and Redundancy Removal in Video Summarization

Over the past decade, research in the field of Content-Based Video Retrieval Systems (CBVRS) has attracted much attention as it encompasses processing of all the other media types i.e. text, image and audio. Video summarization is one of the most important applications as it potentially enables efficient and faster browsing of large video collections. A concise version of the video is often required due to constraints in viewing time, storage, communication bandwidth as well as power. Thus, the task of video summarization is to effectively extract the most important portions of the video, without sacrificing the semantic information in it. The results of video summarization can be used in many CBVRS applications like semantic indexing, video surveillance copied video detection etc. However, the quality of the summarization task depends on two basic aspects: content coverage and redundancy removal. These two aspects are both important and contradictory to each other. This chapter aims to provide an insight into the state-of-the-art approaches used for this booming field of research.

Cellular Automata Algorithms for Digital Image Processing

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

Multilevel Image Segmentation Using Modified Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a well-known swarm optimization technique. PSO is very efficient to optimize the image segmentation problem. PSO algorithm have some drawbacks as the possible solutions may follow the global best solution at one stage. As a result, the probable solutions may bound within that locally optimized solutions. The proposed chapter tries to get over the drawback of the PSO algorithm and proposes a Modified Particle Swarm Optimization (MfPSO) algorithm to segment the multilevel images. The proposed method is compared with the original PSO algorithm and the renowned k-means algorithm. Comparison of the above mentioned existing methods with the proposed method are applied on three real life multilevel gray scale images. For this purpose, three standard objective functions are applied to evaluate the quality of the segmented images. The comparison shows that the proposed MfPSO algorithm is done better than the PSO algorithm and the k-means algorithm to segment the real life multilevel gray scale images.

Feature Extraction

Accessibility problem is relevant for audiovisual information, where enormous data has to be explored and processed. Most of the solutions for this specific type of problems point towards a regular need of extracting applicable information features for a given content domain. And feature extraction process deals with two complicated tasks first deciding and then extracting. There are certain properties expected from good features-Repeatability, Distinctiveness, Locality, Quantity, Accuracy, Efficiency, and Invariance. Different feature extraction techniques are described. The chapter concentrates of taking a survey on the topic of Feature extraction and Image formation. Here both image and video are considered to have their feature extracted. In machine learning, pattern recognition and in image processing has significant contribution. The feature extraction is one of the common mechanisms involved in these two techniques. Extracting feature initiates from an initial data set of measured data and constructs derived informative values which are non redundant in nature.

Integration of Different Analytical Concepts on Multimedia Contents in Service of Intelligent Knowledge Extraction

This chapter will introduce methodology how to use analytical potential of multimedia contents like YouTube, Bing Videos or Vimeo for discovering behavioral consumer characteristics. Chapter will also show how to consolidate unstructured text data sources from blogs and Twitter with revealed knowledge from multimedia contents for better understanding consumer habits and needs. For this purposes Social Network Analysis will be used as well as text mining techniques on different internet data sources. Presented methodology has practical value where information about customer behavior, preferences and changes in preferences during different time periods is valuable information for campaign planning, campaign management and new product development. Presented methodology also captures different techniques for data crawling from different internet resources, as well as analytical consolidation of revealed results which aim is better understanding of client behavior.

Detection of Gradual Transition in Videos

During video editing, the shots composing the video are coalesced together by different types of transition effects. These editing effects are classified into abrupt and gradual changes, based on the inherent nature of these transitions. In abrupt transitions, there is an instantaneous change in the visual content of two consecutive frames. Gradual transitions are characterized by a slow and continuous change in the visual contents occurring between two shots. In this chapter, the challenges faced in this field along with an overview of the different approaches are presented. Also, a novel method for detection of dissolve transitions using a two-phased approach is enumerated. The first phase deals with detection of candidate dissolves by identifying parabolic patterns in the mean fuzzy entropy of the frames. In the second phase, an ensemble of four parameters is used to design a filter which eliminates candidates based on thresholds set for each of the four stages of filtration. The experimental results show a marked improvement over other existing methods.