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.

In recent years, real-time data-oriented applications such as sensor networks, telecommunications data management, network monitoring are required to process various continuous queries on unbounded data streams. A lot of work has been done to deal with the computational complications in constant processing of continuous queries on unbounded, continuous data stream. The K-nearest neighbor algorithm (KNN) is a well-known learning method used in a wide range of problem-solving domains e.g., network monitoring, data mining, and image processing etc. The efficient and scalable processing of multiple continuous queries on dynamic data items requires query indexing and data indexing. Query processing algorithms used on static databases are not well suited to handle dynamic continuous queries over high dimensional data sets.  It is better to build the index for queries which is finite rather than to build the index for data which is infinite. A divide-and-conquer approach is used for indexing and searching for K-nearest neighbors. The approach significantly will reduce the space complexity and will scale well with the increasing data size. The hybrid indexing approach using grid and a K-dimensional tree will reduce the space cost as well searching cost. The data parallelism will provide scalability of continuous queries over high-volume streams.

In recent years, many data-intensive and location based applications have emerged that need to process stream data in applications such as network monitoring, telecommunications data management, and sensor networks. Unlike regular queries, a continuous query exists for certain period of time and need to be continuously processed during this time. The algorithms used for data processing for the traditional database systems are not suited to tackle complex and various continuous queries over dynamic streaming data. The indexing for finite queries is preferred to indexing on infinite data to avoid expensive operations of index maintenance. Previous related work focused on moving queries on static objects or static queries on moving object. But now-a-days queries as well as objects are dynamic. So, hybrid indexing for queries significantly reduces the space costs and scales well with the increasing data. To deal with the speed of unbounded data, it is necessary to use data parallelism in query processing. The data parallelism in query processing offers better performance, availability and scalability.

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