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In the last few decades, content-based image retrieval is considered as one of the most vivid research topics in the field of information retrieval. The limitation of current content-based image retrieval systems is that low-level features are highly ineffective to represent the semantic contents of the image. Most of the research work in content-based image retrieval is focused on bridging the semantic gap between the low-level features and high-level semantic concepts of image. This paper presents a thorough study of different techniques for the reduction of semantic gap. The existing techniques are broadly categorised as: 1) image annotation techniques to define the high-level concepts in image; 2) relevance feedback techniques to integrate user's perception; 3) machine learning and deep learning techniques to associate low-level features with high-level concepts. In addition, the general architecture of semantic-based image retrieval system has been discussed in this survey. This paper also highlights the current and future applications of content-based image retrieval. The paper concludes with promising future research directions.

Human age classification via face images is not only difficult for human being but also challenging for a machine. But, because of potential applications in the field of computer vision, this topic has attracted attention of many researchers. In this paper, a novel two stage age classification framework based on appearance and facial skin ageing features with multi-class support vector machine (M-SVM) is proposed to classify the face images into seven age groups. Appearance features consist of shape features such as, geometric ratios and face angle and facial skin textural features extracted by using local Gabor binary pattern histogram (LGBPH). Facial skin ageing features consist of facial skin textural features and wrinkle analysis. The proposed age classification framework is trained and tested with face images collected from FG-NET ageing database and PAL face database and achieved greatly improved age classification accuracy of 94.45%.

The ageing patterns reflected on the human face with age progression can be used directly to develop age-based intelligent systems for the applications such as biometrics, security and surveillance, etc. This paper presents a novel human age classification system based on local binary patterns (LBP) and wrinkle analysis for ageing feature extraction and multi-class support vector machine (M-SVM) for age classification to classify the face images into four age classes. In this paper, the potential of LBP to capture the minute variations in the textures has been exploited to extract an efficient and robust ageing features invariant to illumination and rotation changes. The proposed age classification system is trained and tested with the face images from PAL face database and achieved the improved age classification accuracy of 91.39%. The experimental results comparison of MSVM classifier with nearest neighbour (k-NN) classifier and artificial neural network (ANN) classifier concludes that M-SVM classifier is the best classifier among the three classifiers for the task of human age classification using LBP and wrinkle analysis for ageing feature extraction.

Human face shows different patterns on the face for particular age class. These ageing patterns on the human faces can be directly used for developing age based systems. Computer based age classification via face images become an interesting topic recently because of their real world applications such as electronic customer relationship management, security and surveillance, etc. In this paper, the detailed survey has been made on the techniques and algorithms developed by researchers for age classification via face images. Comparative study has been done in this paper on existing age classification methods on the basis of pre-processing techniques, ageing feature extraction methods, age classifiers, system performances and databases used for evaluation of system. Future research directions related to the topic of automatic human age classification via face images are also mentioned in this paper.

This paper presents a novel method for human age classification via face images by a computer. The proposed method classifies the human face images into four age groups: child, young, adult and senior adult by using appearance features as ageing features and artificial neural network (ANN) as age classifier. The appearance features consist of both shape and textural features. Only two geometric ratios in combination with newly introduced rotation, scale and translation invariant efficient feature face angle are used as shape features. Local binary pattern histogram (LBPH) of regions of interest in face images are used as textural features. The ANN is designed by using two layer feedforward backpropagation neural networks. The performance of proposed age classification system is evaluated on face images from FG-NET ageing database and achieved greatly improved accuracy of 91.09% and 88.18% for male and female, respectively.