An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences
A growing number of commercially available mobile phones come with integrated high-resolution digital cameras. That enables a new class of dedicated applications to image analysis such as mobile visual search, image cropping, object detection, content-based image retrieval, image classification. In this paper, a new mobile application for image content retrieval and classification for mobile device display is proposed to enrich the visual experience of users. The mobile application can extract a certain number of images based on the content of an image with visual saliency methods aiming at detecting the most critical regions in a given image from a perceptual viewpoint. First, the most critical areas from a perceptual perspective are extracted using the local maxima of a 2D saliency function. Next, a salient region is cropped using the bounding box centred on the local maxima of the thresholded Saliency Map of the image. Then, each image crop feds into an Image Classification system based on SVM and SIFT descriptors to detect the class of object present in the image. ImageNet repository was used as the reference for semantic category classification. Android platform was used to implement the mobile application on a client-server architecture. A mobile client sends the photo taken by the camera to the server, which processes the image and returns the results (image contents such as image crops and related target classes) to the mobile client. The application was run on thousands of pictures and showed encouraging results towards a better user visual experience with mobile displays.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationContent-Based Image Retrievalimage-classificationImage ClassificationImage CroppingImage Retrievalobject-detectionObject DetectionRetrievalMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Image2song: Song Retrieval via Bridging Image Content and Lyric Words
Image is usually taken for expressing some kinds of emotions or purposes, such as love, celebrating Christmas. There is another better way that combines the image and relevant song to amplify the expression, which has dr…
RetrievalTAGContextual Media Retrieval Using Natural Language Queries
The widespread integration of cameras in hand-held and head-worn devices as well as the ability to share content online enables a large and diverse visual capture of the world that millions of users build up collectively…
Natural Language QueriesRetrievalQuebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation
Large Language Models (LLMs) perform outstandingly in various downstream tasks, and the use of the Retrieval-Augmented Generation (RAG) architecture has been shown to improve performance for legal question answering (Nur…
Question AnsweringRAGRetrievalRetrieval-augmented GenerationContent Based Image Indexing and Retrieval
In this paper, we present the efficient content based image retrieval systems which employ the color, texture and shape information of images to facilitate the retrieval process. For efficient feature extraction, we extr…
Content-Based Image RetrievalEdge DetectionImage CompressionImage Retrieval+1Developing a Dataset for Evaluating Approaches for Document Expansion with Images
Motivated by the adage that a {``}picture is worth a thousand words{''} it can be reasoned that automatically enriching the textual content of a document with relevant images can increase the readability of a document. M…
Information RetrievalRetrieval