Numerous research works about the extraction of low-level features from images and videos have been published. However, only recently the focus has shifted to exploiting low-level features to classify images and videos automatically into semantically broad and meaningful categories. In this paper, novel classification algorithms are presented for three broad and generalpurpose categories. In detail, we present algorithms for distinguishing photo-like images from graphical images, actual photos from only photo-like, but artificial images and presentation slides/scientific posters from comics. On a large image database, our classification algorithm achieved an accuracy of 97.69 % in separating photo-like images from graphical images. In the subset of photo-like images, true photos could be separated from ray-traced/rendered image with an accuracy of 97.3%, while with an accuracy of 99.5 % the subset of graphical images was successfully partitioned into presentation slides/scientific posers and comics. 1.

Web image search is difficult in part because a handful of keywords are generally insufficient for characterizing the visual properties of an image. Popular engines have begun to provide tags based on simple characteristics of images (such as tags for black and white images or images that contain a face), but such approaches are limited by the fact that it is unclear what tags end-users want to be able to use in examining Web image search results. This paper presents CueFlik, a Web image search application that allows end-users to quickly create their own rules for re-ranking images based on their visual characteristics. End-users can then re-rank any future Web image search results according to their rule. In an experiment we present in this paper, end-users quickly create effective rules for such concepts as “product photos”, “portraits of people”, and “clipart”. When asked to conceive of and create their own rules, participants create such rules as “sports action shot ” with images from queries for “basketball ” and “football”. CueFlik represents both a promising new approach to Web image search and an important study in end-user interactive machine learning.

Interaction with large unstructured datasets is difficult because existing approaches, such as keyword search, are not always suited to describing concepts corresponding to the distinctions people want to make within datasets. One possible solution is to allow end-users to train machine learning systems to identify desired concepts, a strategy known as interactive concept learning. A fundamental challenge is to design systems that preserve end-user flexibility and control while also guiding them to provide examples that allow the machine learning system to effectively learn the desired concept. This paper presents our design and evaluation of four new overview-based approaches to guiding example selection. We situate our explorations within CueFlik, a system examining end-user interactive concept learning in Web image search. Our evaluation shows our approaches not only guide end-users to select better training examples than the best-performing previous design for this application, but also reduce the impact of not knowing when to stop training the system. We discuss challenges for end-user interactive concept learning systems and identify opportunities for future research on the effective design of such systems. ACM Classification: H5.2 [Information Interfaces and

1.1 OCR: the Motivation Optical Character Recognition (OCR) is a branch of pattern recognition, and also a branch of computer vision. OCR has been extensively researched for more than four decades. With the advent of digital computers, many researchers and engineers have been

Holistic representations of natural scenes are an effective and powerful source of information for semantic classification and analysis of images. Despite the technological hardware and software advances, consumer single sensor imaging devices technology are quite far from the ability of recognizing scenes and/or to exploit the visual content during (or after) acquisition time. The frequency domain has been successfully exploited to holistically encode the content of natural scenes in order to obtain a robust representation for scene classification. In this paper we exploit a holistic representation of the scene in the DCT domain fully compatible with the JPEG format. The advised representation is coupled with a logistic classifier to perform classification of the scene at superordinate level of description (e.g., Natural vs. Artificial), or to discriminate between multiple classes of scenes usually acquired by a consumer imaging device (e.g., Portrait, Landscape and Document). The proposed method is able to work in constrained domain. Experiments confirm the effectiveness of the proposed method. The obtained results closely match state of the art method in terms of accuracy outperforming in terms of computational resources.