  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.
