Sparse Matrix-based Random Projection for Classification

As a typical dimensionality reduction technique, random projection can be simply implemented with linear projection, while maintaining the pairwise distances of high-dimensional data with high probability. Considering this technique is mainly exploited for the task of classification, this paper is developed to study the construction of random matrix from the viewpoint of feature selection, rather than of traditional distance preservation. This yields a somewhat surprising theoretical result, that is, the sparse random matrix with exactly one nonzero element per column, can present better feature selection performance than other more dense matrices, if the projection dimension is sufficiently large (namely, not much smaller than the number of feature elements); otherwise, it will perform comparably to others. For random projection, this theoretical result implies considerable improvement on both complexity and performance, which is widely confirmed with the classification experiments on both synthetic data and real data

An Overview of Advances of Pattern Recognition Systems in Computer Vision

26 pagesFirst of all, let's give a tentative answer to the following question: what is pattern recognition (PR)? Among all the possible existing answers, that which we consider being the best adapted to the situation and to the concern of this chapter is: "pattern recognition is the scientific discipline of machine learning (or artificial intelligence) that aims at classifying data (patterns) into a number of categories or classes". But what is a pattern? A pattern recognition system (PRS) is an automatic system that aims at classifying the input pattern into a specific class. It proceeds into two successive tasks: (1) the analysis (or description) that extracts the characteristics from the pattern being studied and (2) the classification (or recognition) that enables us to recognise an object (or a pattern) by using some characteristics derived from the first task

The Fallacy of Informed Consent: Linguistic Markers of Assent and Contractual Design in Some E-User Agreements

Orthodox contract law theory assumes that parties agree to the terms of a contract before entering into an agreement. However, recent factual evidence points towards the fact that consumers do not systematically read, and thus become informed of, the terms of a contract. Academics are asking for mandatory frameworks to ensure that informed consent is indeed sought and given by parties to a contract. The present study looks into the user agreements of four online companies that provide a marketplace for the sale of goods or free provision of services by other sellers and/or users (Ebay, Tripadvisor, YouTube and Amazon). The aim is firstly to identify the lexical/textual markers and peri-textual features of agreement in order to highlight the fallacy of informed consent. Secondly, the paper lists textual and peri-textual alternative contractual design (here called counter-design) in online user agreement. In so doing, contractual design features are distinguished from nudges. Suggested counter-design features help make informed consent effective

An improved feature vector for content-based image retrieval in DCT domain

International audienceThis paper proposes an improved approach for content-based image retrieval in Discrete Cosine Transform domain. For each 4x4 DCT block, we calculate the statistical information of three groups of AC coefficients and propose to use these information to form the AC-Pattern and use DC coefficients of neighboring blocks to construct DC-Pattern. The histograms of these two patterns are constructed and their selections are concatenated as feature descriptor. Similarity between the feature descriptors is measured by chi-squared distance. Experiments executed on widely used face and texture databases show that better performance can be observed with the proposal compared with other classical method and state-of-the-art approaches

SEMANTIC SEGMENTATION VIA SPARSE CODING OVER HIERARCHICAL REGIONS

International audienceThe purpose of this paper is segmenting objects in an image and assigning a predefined semantic label to each object. There are two areas of novelty in this paper. On one hand, hierarchical regions are used to guide semantic segmenta-tion instead of using single-level regions or multi-scale regions generated by multiple segmentations. On the other hand, sparse coding is introduced as high level description of the regions, which contributes to less quantization error than traditional bag-of-visual-words method. Experiments on the challenging Microsoft Research Cambridge dataset (MSRC 21) show that our algorithm achieves state-of-the-art performance

Shape-based invariant features extraction for object recognition

International audienceThe emergence of new technologies enables generating large quantity of digital information including images; this leads to an increasing number of generated digital images. Therefore it appears a necessity for automatic systems for image retrieval. These systems consist of techniques used for query specification and re-trieval of images from an image collection. The most frequent and the most com-mon means for image retrieval is the indexing using textual keywords. But for some special application domains and face to the huge quantity of images, key-words are no more sufficient or unpractical. Moreover, images are rich in content; so in order to overcome these mentioned difficulties, some approaches are pro-posed based on visual features derived directly from the content of the image: these are the content-based image retrieval (CBIR) approaches. They allow users to search the desired image by specifying image queries: a query can be an exam-ple, a sketch or visual features (e.g., colour, texture and shape). Once the features have been defined and extracted, the retrieval becomes a task of measuring simi-larity between image features. An important property of these features is to be in-variant under various deformations that the observed image could undergo. In this chapter, we will present a number of existing methods for CBIR applica-tions. We will also describe some measures that are usually used for similarity measurement. At the end, and as an application example, we present a specific ap-proach, that we are developing, to illustrate the topic by providing experimental results

A NEW DESCRIPTOR BASED ON 2D DCT FOR IMAGE RETRIEVAL

International audienceContent-based image retrieval relies on feature comparison between images. So the selection of feature vector is important. As many images are compressed by transforms, constructing the feature vector directly in transform domain is a very popular topic. We propose a new feature vector in DCT domain. Our method selects part of DCT coefficients inside each block to construct AC-Pattern and use DC coefficients between neighboring blocks to construct DC-Pattern. Two histograms are formed and parts of them are used to build a descriptor vector integrating features to do image retrieval. Experiments are done both on face image databases and texture image database. Compared to other methods, results show that we can get better performance on both face and texture database by using the proposed method

SEMANTIC SEGMENTATION VIA SPARSE CODING OVER HIERARCHICAL REGIONS

International audienceThe purpose of this paper is segmenting objects in an image and assigning a predefined semantic label to each object. There are two areas of novelty in this paper. On one hand, hierarchical regions are used to guide semantic segmenta-tion instead of using single-level regions or multi-scale regions generated by multiple segmentations. On the other hand, sparse coding is introduced as high level description of the regions, which contributes to less quantization error than traditional bag-of-visual-words method. Experiments on the challenging Microsoft Research Cambridge dataset (MSRC 21) show that our algorithm achieves state-of-the-art performance