Contextual Bag-Of-Visual-Words and ECOC-Rank for Retrieval and Multi-class Object Recognition

Projecte Final de Màster UPC realitzat en col.laboració amb Dept. Matemàtica Aplicada i Anàlisi, Universitat de BarcelonaMulti-class object categorization is an important line of research in Computer Vision and Pattern Recognition fields. An artificial intelligent system is able to interact with its environment if it is able to distinguish among a set of cases, instances, situations, objects, etc. The World is inherently multi-class, and thus, the eficiency of a system can be determined by its accuracy discriminating among a set of cases. A recently applied procedure in the literature is the Bag-Of-Visual-Words (BOVW). This methodology is based on the natural language processing theory, where a set of sentences are defined based on word frequencies. Analogy, in the pattern recognition domain, an object is described based on the frequency of its parts appearance. However, a general drawback of this method is that the dictionary construction does not take into account geometrical information about object parts. In order to include parts relations in the BOVW model, we propose the Contextual BOVW (C-BOVW), where the dictionary construction is guided by a geometricaly-based merging procedure. As a result, objects are described as sentences where geometrical information is implicitly considered. In order to extend the proposed system to the multi-class case, we used the Error-Correcting Output Codes framework (ECOC). State-of-the-art multi-class techniques are frequently defined as an ensemble of binary classifiers. In this sense, the ECOC framework, based on error-correcting principles, showed to be a powerful tool, being able to classify a huge number of classes at the same time that corrects classification errors produced by the individual learners. In our case, the C-BOVW sentences are learnt by means of an ECOC configuration, obtaining high discriminative power. Moreover, we used the ECOC outputs obtained by the new methodology to rank classes. In some situations, more than one label is required to work with multiple hypothesis and find similar cases, such as in the well-known retrieval problems. In this sense, we also included contextual and semantic information to modify the ECOC outputs and defined an ECOC-rank methodology. Altering the ECOC output values by means of the adjacency of classes based on features and classes relations based on ontologies, we also reporteda significant improvement in class-retrieval problems

An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "forget" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithm--the dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests the choice of activation function should always be cross-validated

Contextual Bag-Of-Visual-Words and ECOC-Rank for Retrieval and Multi-class Object Recognition

Projecte Final de Màster UPC realitzat en col.laboració amb Dept. Matemàtica Aplicada i Anàlisi, Universitat de BarcelonaMulti-class object categorization is an important line of research in Computer Vision and Pattern Recognition fields. An artificial intelligent system is able to interact with its environment if it is able to distinguish among a set of cases, instances, situations, objects, etc. The World is inherently multi-class, and thus, the eficiency of a system can be determined by its accuracy discriminating among a set of cases. A recently applied procedure in the literature is the Bag-Of-Visual-Words (BOVW). This methodology is based on the natural language processing theory, where a set of sentences are defined based on word frequencies. Analogy, in the pattern recognition domain, an object is described based on the frequency of its parts appearance. However, a general drawback of this method is that the dictionary construction does not take into account geometrical information about object parts. In order to include parts relations in the BOVW model, we propose the Contextual BOVW (C-BOVW), where the dictionary construction is guided by a geometricaly-based merging procedure. As a result, objects are described as sentences where geometrical information is implicitly considered. In order to extend the proposed system to the multi-class case, we used the Error-Correcting Output Codes framework (ECOC). State-of-the-art multi-class techniques are frequently defined as an ensemble of binary classifiers. In this sense, the ECOC framework, based on error-correcting principles, showed to be a powerful tool, being able to classify a huge number of classes at the same time that corrects classification errors produced by the individual learners. In our case, the C-BOVW sentences are learnt by means of an ECOC configuration, obtaining high discriminative power. Moreover, we used the ECOC outputs obtained by the new methodology to rank classes. In some situations, more than one label is required to work with multiple hypothesis and find similar cases, such as in the well-known retrieval problems. In this sense, we also included contextual and semantic information to modify the ECOC outputs and defined an ECOC-rank methodology. Altering the ECOC output values by means of the adjacency of classes based on features and classes relations based on ontologies, we also reporteda significant improvement in class-retrieval problems

The Structure of Language in Vernacular Architecture

The creation of a work in architecture can be compared with grammar considering the structure and constituent elements of the speech language including the phoneme, notion and its disciplinary principles. Syntagmatic and paradigmatic are considered as two fundamental principles of the speech by providing several signification messages to form daily conversation and to create a work or offer a mot message by various innovation. The creation of a work can be found in two different structure including form and pattern language. Form language includes visual elements which organize the form of a work while pattern language constitutes the signification to order visual elements toward designer’s thought. The present study considers the natural structural language and its components and then compares the findings with pattern language according to various structures to form a vernacular architecture for a house including form language and pattern language. Form language considers how to make a work and its components due to differences in environmental conditions and pattern language represents the codes of message subject to values, goals, customs and tradition of society that are altered based on the presence of human beings in space and his thoughtful manipulation in nature

Dry Matter and Essential Oil Yield Changes of Lavandula officinalis under Cowmanure and Vermicompost Application

In order to study the effect of organic fertilizer on shoot yield and essential oil content of lavender, this experiment was conducted in the Research Institute of Forest and Rangelands, Karaj, Iran, in 2013-14. The treatment groups consisted of vermicompost (0, 5, 10 and 15 ton/ha) and cow manure (0, 10, 20 and 30 ton/ha). The experimental design was a factorial experiment based on randomized complete block design (RCBD) with three replications. The resultsshowed that cow manureapplication significantly affected big and smallcanopy diameter, canopy perimeter, lateral stems number and woody stem yield (P≤0.01). Moreover, manure significantly affected main stem diameter and leaf yield (P≤0.05). Results indicated that vermicompost application significantly affected big and smallcanopy diameter, annual stem number, leaf yield, annual stem yield, woody stem yield, total biological yield, essential oil yield (P≤ 0/01), and main stem (P≤ 0/05). According to the results, the interaction effect of treatments was significant for total shoot and leaf yield (P≤ 0/05). The highest sub stem number (24 n/p) was obtainedin 30 ton/ha manure treatment. While, the highest leaf yield (2206.4 kg/ha), annual stem yield (7133.2 kg/ha), annual branches yield (9933/6 kg/ha), total biological yield (1333.6 kg/ha) and essential oil yield (82.67 kg/ha) were determined at 15 ton/have vermicompost treatment. These fertilizers can improve tiller number and lateral stems growth but not affect essential oil percent and yield. It seems that they can increase it in drought stress condition because of improving soil moisture and fertility

Chemical Composition of Essential Oil from Leaves, Stems, Flowers and Seeds of Heracleum rechingeri Manden. from Iran

Chemical composition of the essential oils extracted from the leaves, stems, flowers and seeds of  Heracleum rechingeri Manden. were analyzed by GC and GC/MS. Twenty-four components were characterized for the leaf oil with Octyl acetate(47.2%), Octanol(15.2%) and E-caryophyllen (5.7%) as the main constituents. 25 compounds were identified in the stem oil with Elemicin (65.3%), Octyl acetate (13.0%) and Octanol (3.5%) as the major components. 18 compound were identified in the  flower oil with Octyl acetate (46.8%), Elemicin (12.8%) and methyl chavicol (10.2%) as the major components. Among 13 compounds studying in the seed oil of H.rechingeri, the major constituents were Octyl acetate (91.7%), Octanol (3.5%) and Octanal (1.2%)