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Building efficient Image Features with optimal dimension and high discriminative power is important in processing the digital information hidden inside the images. Image Retrieval systems use the feature vectors to retrieve similar images based on feature vector similarity. Image Feature Vector generation is important step in CBIR. Most of the CBIR use Linear Data structure for Feature Vector. In this paper the Hierarchical data Structure: Binary Tree is used for Feature vector creation in Image Retrieval Systems. Images in Spatial Domain are considered for Feature Vector Generation. Low-level Feature: Color Descriptors are used as Image Contents, to represents the image in feature vector form. Performance of the image retrieval is tested for Color Images. It has been observed that, along with the Dimensionally reduction, the proposed approach of Feature Vector generation, improves the discriminative power of Feature Vector. For RGB colorspaces the cascaded statistical features, when stores in hierarchical data structure, this provides the facility to perform the calculations in parallel manner also capable to keep discriminative power comparable w.r.t size of feature vector.

Cognitive computing is an intelligent system that converses with and mimics the human being in a natural form by learning at scale, reasoning with purpose. Cognitive computing is the third era of computing and now cognitive computing has attracted considerable attention in both academia and industry. Machines and humans’ intelligence gets combined to solve the most complex problems of the world. Complicated problems can be solved by computing framework without intervention of humans. Natural language processing with emotion analysis, artificial intelligence (AI), machine learning, neural networks are building blocks of cognitive computing process to tackle problems as the way human beings do. Nowadays, advance technologies adapt cognitive computing in many areas to assist human experts in smart decision making for the betterment of businesses. Current technology expectations are to make human life better and to help them work in better ways. Nowadays, there is explosive data growth, business conditions are also changing rapidly so intelligent, hassle-free, and enhanced interactions amongst human beings and technology can be effectively addressed by cognitive systems. AI is in use in many apps like the Alexa: Amazon voice assistant, Netflix and Amazon algorithms which recommend the next to watch or buy. Some examples of personal assistants that uses cognitive computing are Alexa, Siri, Google assistant, and Cortana. Advancement of technology and its adoption in the public and private sectors will greatly affect the future of cognitive computing due to technology evolutionary paths and trends. Cognitive systems must be adaptive, interactive, iterative, and stateful and contextual in commercial and widespread applications. Some of the applications that can use cognitive computing to gain benefits from this type of technology are cognitive computing for changing business values, Financial and Investment firms, Healthcare and veterinary medicine, Travel and Tourism, Health and wellness, Education and learning, Agriculture, Communication and network technology.

Image Retrieval systems highly rely on the image signatures stored in database. Constructing Image signatures with optimal size and accurate representation of image is most interesting challenge here. High level image features like object and their characteristic are useful in sentiment image analysis but faces the limitations of domain specific feature vector. Low level image features like color, texture and shape are interesting and useful enough to represent the image in diverse image databases. Targeting to low level image feature: color, the image signatures created are of huge size, as those represents three color planes and their values. Image signatures vary, as the image size varies. Targeting towards creating the optimal image signatures with color feature, to reduce the size of feature vector is possible by considering images in frequency domain. Image transforms converts image in frequency domain, with compressed image data. This data is further reduced form by ignoring the low energy components. This paper discusses the approach to construct image signatures by considering high energy components of transformed image. Further the high energy components are bagged together. Here the intelligent mean-count tree is created based on image information. Performance of Image retrieval is tested using image feature database.

Feature extraction of images are crucial in image retrieval systems. Many approaches are stated and proved by researchers for image feature extraction and processing. Research is being done from low-level feature extraction toward high-level feature extraction. This paper discusses the feature extraction from the DFT transformed color images in multiple color planes. DFT image transform provides effective way to differentiate the image textures. For dimensionality reduction statistical parameters such as kurtosis, standard deviation, and variance are used for feature vector generation. Euclidian distance is used in the proposed approach. Four different types of feature vectors are created and tested for each image class. The images are retrieved based on the image pixel values of DFT phase information and DFT magnitude information of different color spaces like RGB, YIQ, HSV, and YCbCr similar to that of image class. Image retrieval performance of the proposed approach is compared for database of 1000 images of ten different categories. Precision of image retrieval is above 60% for all classes and more than 80% for some of the image classes.

With the advancement of Image acquisition and storing, image retrieval has been proven as the research problem. Many approaches for image retrieval, has been stated by researchers to solve the image retrieval problem. In this paper we state DFT transform based approach for image retrieval using Image classes. Here formation of Feature vectors of the Images is based on Color based DFT Phase information of images those belongs to same class. DFT Image transform provides effective way to differentiate the image textures. Particularly Phase part of DFT carries the important information about the objects in image. In the proposed approach of image retrieval, DFT phase information is used for representing the images using feature vector effectively. To make image retrieval more accurate, class wise images are considered for creation of database feature vectors. As, images belonging same class are content wise similar, the generalized feature vector is produced for each class Generalized feature vectors represents all images of that class. Cosine correlation similarity measure is used in the proposed approach. 4 Different types of feature vectors are created and tested for each image class. The Images are retrieved based on the feature vector values of DFT Phase information of RGB's planes with similar to that of Feature vector of Image class. Image retrieval Performance of the proposed approach is compared for database of 1000 images of 10 different categories. Average Accuracy of Image retrieval is above 60% for all classes and more than 75% for some of the image classes.