Abstract Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between low-level primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features... Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between low-level primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations. Read More Read Less

Abstract This paper presents a systematic methodology to analyze displacement metrics to be used in recognizing facial expressions with the help of FAP or landmarks on a face particularly on the mouth, nose tip and eye and eye brows. The proposed system tries to automatically perform human face detection, feature point extraction for the further purpose of facial expression recognition stably and robustly over... This paper presents a systematic methodology to analyze displacement metrics to be used in recognizing facial expressions with the help of FAP or landmarks on a face particularly on the mouth, nose tip and eye and eye brows. The proposed system tries to automatically perform human face detection, feature point extraction for the further purpose of facial expression recognition stably and robustly over live webcam input of real time sequences of frames and yield promising performance in low-resolution video sequences captured in real-world environments. Thus a sequential three-stage approach is taken, firstly Face Detection, secondly Feature Extraction and finally tracking of the extracted features. In real time, a human face is detected using an improved Haar based algorithm. Then facial features are extracted using a combination of appearance and geometrical approach depending on the ROI extracted by Haar based algorithm. The tracking is done with the help of pyramidal Lucas Kanade algorithm. Several experiments conducted under relatively non-controlled conditions demonstrate the accuracy and robustness of the approach. Read More Read Less

Abstract Speech Emotion recognition is one of the most popular and widely discussed topics in the present world. Every day human-being shows different types of emotions. In this paper we propose a new technique which can distinguish two emotion states by analyzing speech signals. The quantification is done by fitting an ‘ellipsoid’ on the reconstructed attractor obtained from the speech signals in two different... Speech Emotion recognition is one of the most popular and widely discussed topics in the present world. Every day human-being shows different types of emotions. In this paper we propose a new technique which can distinguish two emotion states by analyzing speech signals. The quantification is done by fitting an ‘ellipsoid’ on the reconstructed attractor obtained from the speech signals in two different emotional conditions. Our experiments shows satisfactory results in this context. Read More Read Less