A Survey on Classification of Photo Aesthetics Based on Emotion

Recognition of human facial expression and calculating exact emotion by computer vision is an interesting and challenging problem. Emotion in natural scenery images plays vital role in the way humans perceive an image. Based on the various emotions like happiness, sadness, fear, anger of any human being the images that are examined by that person can propose that if the person is in happy mood then he/she would C the same images in different ways but still can be possible to build a universal classification for various emotions. The paper proposes the various techniques of recognizing emotion on the basis of how humans perceive an image, also aims to classify the aesthetics of the photographic images and determine wallpaper (Scene or non-scene images) according to human emotions

Improvised Salient Object Detection and Manipulation

In case of salient subject recognition, computer algorithms have been heavily relied on scanning of images from top-left to bottom-right systematically and apply brute-force when attempting to locate objects of interest. Thus, the process turns out to be quite time consuming. Here a novel approach and a simple solution to the above problem is discussed. In this paper, we implement an approach to object manipulation and detection through segmentation map, which would help to desaturate or, in other words, wash out the background of the image. Evaluation for the performance is carried out using the Jaccard index against the well-known Ground-truth target box technique.Comment: 7 page

Recognizing and Curating Photo Albums via Event-Specific Image Importance

Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection. In this paper, we attempt to simultaneously solve both tasks: album-wise event recognition and image- wise importance prediction. We collected an album dataset with both event type labels and image importance labels, refined from an existing CUFED dataset. We propose a hybrid system consisting of three parts: A siamese network-based event-specific image importance prediction, a Convolutional Neural Network (CNN) that recognizes the event type, and a Long Short-Term Memory (LSTM)-based sequence level event recognizer. We propose an iterative updating procedure for event type and image importance score prediction. We experimentally verified that image importance score prediction and event type recognition can each help the performance of the other.Comment: Accepted as oral in BMVC 201