Barcode reading mobile applications to identify products from pictures acquired by mobile devices are widely used by customers from all over the world to perform online price comparisons or to access reviews written by other customers. Most of the currently available 1D barcode reading applications focus on effectively decoding barcodes and treat the underlying detection task as a side problem that needs to be solved using general purpose object detection methods. However, the majority of mobile devices do not meet the minimum working requirements of those complex general purpose object detection algorithms and most of the efficient specifically designed 1D barcode detection algorithms require user interaction to work properly. In this work, we present a novel method for 1D barcode detection in camera captured images, based on a supervised machine learning algorithm that identifies the characteristic visual patterns of 1D barcodes' parallel bars in the two-dimensional Hough Transform space of the processed images. The method we propose is angle invariant, requires no user interaction and can be effectively executed on a mobile device; it achieves excellent results for two standard 1D barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the performance of a state-of-the-art 1D barcode reading library by coupling it with our detection method.

In this paper we approach the task of figure-ground segmentation of natural images using a novel framework to generate highly collaborative tree-based structures, called High Entropy Ensembles (HEE).While many model combination frameworks adopt rejection rules to improve the classification time of the ensembles at the cost of restricting the interactions between the different elements in the structures, throughout our work we prove that, similarly to the Cascade Classification Model [3], when execution time is not critical, better results can be obtained when encouraging that kind of interaction by combining heterogeneous suboptimal classifiers into highly connected tree-based ensembles in which the different algorithms communicate with each other to let the strengths of one overcome the weaknesses of the others and vice versa. Inspired by randombased model combination approaches [2], we do not focus on looking for the optimal classifiers to be added to the HEE, instead we pick them from a pool of randomly configured segmentation algorithms. This randomness injection increases the effectiveness of HEE while also decreasing both the computational complexity of the model creation procedure and the risk of overfitting the training data, which is a common issue for most model combination frameworks.

The rapid growth of Web information led to an increasing amount of user-generated content, such as customer reviews of products, forum posts and blogs. In this paper we face the task of assigning a sentiment polarity to user-generated short documents to determine whether each of them communicates a positive or negative judgment about a subject. The method we propose exploits a Growing Hierarchical SelfOrganizing Map to obtain a sparse encoding of user-generated content. The encoded documents are subsequently given as input to a Support Vector Machine classifier that assigns them a polarity label. Unlike other works on opinion mining, our model does not use a priori hypotheses involving special words, phrases or language constructs typical of certain domains. Using a dataset composed by customer reviews of products, the experimental results we obtain are close to those achieved by other recent works.

Barcode reading mobile applications that identify products from pictures taken using mobile devices are widely used by customers to perform online price comparisons or to access reviews written by others. Most of the currently available barcode reading approaches focus on decoding degraded barcodes and treat the underlying barcode detection task as a side problem that can be addressed using appropriate object detection methods. However, the majority of modern mobile devices do not meet the minimum working requirements of complex general purpose object detection algorithms and most of the efficient specifically designed barcode detection algorithms require user interaction to work properly. In this paper, we present a novel method for barcode detection in camera captured images based on a supervised machine learning algorithm that identifies one-dimensional barcodes in the two-dimensional Hough Transform space. Our model is angle invariant, requires no user interaction and can be executed on a modern mobile device. It achieves excellent results for two standard one-dimensional barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the overall performance of a state-of-the-art barcode reading algorithm by combining it with our detection method.

This work proposes a new error backpropagation approach as a systematic way to configure and train the Multi-net System MNOD, a recently proposed algorithm able to segment a class of visual objects from real images. First, a single node of the MNOD is configured in order to best resolve the visual object segmentation problem using the best combination of parameters and features. The problem is then how to add new nodes in order to improve accuracy and avoid overfitting situations. In this scenario, the proposed approach employs backpropagation of error maps to add new nodes with the aim of increasing the overall segmentation performance. Experiments conducted on a standard dataset of real images show that our configuration method, using only simple edges and colors descriptors, leads to configurations that produced comparable results in visual objects segmentation.

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The proposed model aims to extend the MNOD algorithm adding a new type of node specialized in object classification. For each potential object identified by the MNOD, a set of segments are generated using a min-cut based algorithm with different seeds configurations. These segments are classified by a suitable neural model and then the one with higher value is chosen, in agreement with a proper energy function. The proposed method allows to segment and classify each object simultaneously. The results showed in the experiment section highlight the potential and the cost of having unified segmentation and classification in a single model.

In this study we propose a new strategy to perform an object segmentation using a multi neural network approach. We started extending our previously presented object detection method applying a new segment based classification strategy. The result obtained is a segmentation map post processed by a phase that exploits the GrabCut algorithm to obtain a fairly precise and sharp edges of the object of interest in a full automatic way. We tested the new strategy on a clothing commercial dataset obtaining a substantial improvement on the quality of the segmentation results compared with our previous method. The segment classification approach we propose achieves the same improvement on a subset of the Pascal VOC 2011 dataset which is a recent standard segmentation dataset, obtaining a result which is inline with the state of the art.

In this study we propose a mobile application which interfaces with a Content-Based Image Retrieval engine for online shopping in the fashion domain. Using this application it is possible to take a picture of a garment to retrieve its most similar products. The proposed method is firstly presented as an application in which the user manually select the name of the subject framed by the camera, before sending the request to the server. In the second part we propose an advanced approach which automatically classifies the object of interest, in this way it is possible to minimize the effort required by the user during the query process. In order to evaluate the performance of the proposed method, we have collected three datasets: the first contains clothing images of products taken from different online shops, whereas for the other datasets we have used images and video frames of clothes taken by Internet users. The results show the feasibility in the use of the proposed mobile application in a real scenario.

The objective of this work is the realization of an algorithm to provide a query suggestion feature in order to support the search engine of a commercial web site.Starting from web server logs, our solution creates a model analyzing the queries submitted by the users.Given a submitted query, the system searches the most adequate queries to suggest.Our method implements an already known session based proposal enriching it by exploiting specific information available in the current context: the category the user is browsing on the web site and a solution to overcome the limits of a pure session based approach considering also similarity between queries.Quantitative and qualitative experiments show that the proposed model is suitable in terms of resources employed and user's satisfaction degree.

Nowadays an increasing number of people own mobile phones with built-in camera, able to take pictures. Thus, having a fast and fully automatic algorithm of image retrieval is considered a promising way to identify plant leaves on a mobile device. Our solution proposes a Support Vector Machine that provides a multi-class probability estimation with radial basis function kernel based on two descriptors: PHOG and a vari- ant of HAAR. With our method we placed seventh among all the fully automatic methods who participated in the ImageCLEF Plant Identi- cation 2012