A binary neural k-nearest neighbour technique

K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering data. K-NN is effective but is often criticised for its polynomial run-time growth as k-NN calculates the distance to every other record in the data set for each record in turn. This paper evaluates a novel k-NN classifier with linear growth and faster run-time built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and real-valued data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall the k-best matches. We compare various configurations of the binary approach to a conventional approach for memory overheads, training speed, retrieval speed and retrieval accuracy. We demonstrate the superior performance with respect to speed and memory requirements of the binary approach compared to the standard approach and we pinpoint the optimal configurations

PENGGUNAAN FITUR WARNA DAN TEKSTUR UNTUK CONTENT BASED IMAGE RETRIEVAL CITRA BUNGA

Pencarian gambar berdasarkan gambar pada database, seringkali dilakukan untuk mengatasi duplikasi pada suatu karya. Content Based Image Retrieval (CBIR) Citra Bunga adalah engine pada komputer untuk melakukan pencarian gambar berdasarkan gambar pada database. Penelitian pada Content Based Image Retrieval (CBIR) Citra Bunga telah dilakukan oleh banyak peneliti. Permasalahan terjadi ketika memilih metode pendekatan seperti preprocessing, ekstraksi fitur dan similarity measure pada CBIR Citra Bunga. Pendekatan yang tidak sesuai dengan data yang diuji, tidak akan memberikan hasil yang optimal. Untuk mengetahui tingkat keberhasilan pendekatan yang digunakan pada CBIR Citra Bunga, digunakan perhitungan nilai precision. Pada penelitian ini, dataset yang akan digunakan adalah dataset Oxford Flower 17. Berdasarkan penelitian sebelumnya, untuk mendapatkan nilai precision yang lebih baik, penelitian ini akan menggunakan ekstraksi fitur warna Hue Saturation Value (HSV), ekstraksi fitur tekstur Gray Level Co-occurrence Matrix (GLCM), dan gabungan kedua fitur dengan pendekatan histogram. Pada penelitian CBIR Citra Bunga ini, terdapat tiga proses yaitu segmentasi menggunakan thresholding, proses ekstraksi fitur, dan pengukuran tingkat kemiripan citra dengan Euclidean Distance. Pengujian pada sistem dilakukan berdasarkan citra yang tersegmentasi dan tidak tersegmentasi. Pengujian sistem dengan hasil Mean Average Precision (MAP) terbesar dihasilkan oleh proses ekstraksi fitur GLCM tidak tersegmentasi sebesar 87,32%, dan untuk nilai MAP terbesar pada citra tersegmentasi dihasilkan pada proses ekstraksi fitur HSV sebesar 83,35%. Kata kunci: Content Based Image Retrieval, ekstraksi fitur HSV, ekstraksi fitur GLCM, thresholding, Euclidean Distance, Mean Average Precision (MAP);--- Searching images based on images in the database, often done to overcome duplication of a work. Content Based Image Retrieval (CBIR) Flower Image is the engine on the computer To perform image-based image search on the database. Research on Content Based Image Retrieval (CBIR) Flower Image has been done by many researchers. Problems occur when choosing approaches such as preprocessing, feature extraction and similarity measure in CBIR Flower Image. Approaches which don't correspond with the data image test, would not provide optimal results. To know the success rate of approach used in CBIR Flower Image, the calculation of precision value is used. In this study, the dataset that will be used is dataset Oxford Flower 17. Based on previous research, to get better precision value, this research will use Hue Saturation Value (HSV) feature extraction, feature extraction of Gray Level Co-occurrence Matrix (GLCM) texture, and combination of both features with histogram approach. In this research, there are three processes: segmentation using thresholding, feature extraction process, and measurement of image similarity level with Euclidean Distance. For testing the system, is based on segmented image and non-segmented image. The result of the largest Mean Average Precision (MAP) produced in this study, resulted from the process of unsegmented image by the GLCM feature extraction of 87.32%, and for the largest MAP value in the segmented image produced by the HSV feature extraction process of 83.35%. Keywords: Content Based Image Retrieval, feature extraction HSV, feature extraction GLCM, thresholding, Euclidean Distance, Mean Average Precision (MAP

Unsupervised Generative Adversarial Cross-modal Hashing

Cross-modal hashing aims to map heterogeneous multimedia data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Unsupervised cross-modal hashing is more flexible and applicable than supervised methods, since no intensive labeling work is involved. However, existing unsupervised methods learn hashing functions by preserving inter and intra correlations, while ignoring the underlying manifold structure across different modalities, which is extremely helpful to capture meaningful nearest neighbors of different modalities for cross-modal retrieval. To address the above problem, in this paper we propose an Unsupervised Generative Adversarial Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for unsupervised representation learning to exploit the underlying manifold structure of cross-modal data. The main contributions can be summarized as follows: (1) We propose a generative adversarial network to model cross-modal hashing in an unsupervised fashion. In the proposed UGACH, given a data of one modality, the generative model tries to fit the distribution over the manifold structure, and select informative data of another modality to challenge the discriminative model. The discriminative model learns to distinguish the generated data and the true positive data sampled from correlation graph to achieve better retrieval accuracy. These two models are trained in an adversarial way to improve each other and promote hashing function learning. (2) We propose a correlation graph based approach to capture the underlying manifold structure across different modalities, so that data of different modalities but within the same manifold can have smaller Hamming distance and promote retrieval accuracy. Extensive experiments compared with 6 state-of-the-art methods verify the effectiveness of our proposed approach.Comment: 8 pages, accepted by 32th AAAI Conference on Artificial Intelligence (AAAI), 201

Ad-hoc Biomedical Information Retrieval for Global Pandemics: A Study of Methods Based on the TREC-COVID test collection

openThe TREC_COVID Challenge has the goal to create search engines to effectively and efficiently retrieve information produced at a rate never seen before, in the biomedical field. This work focuses on the effectiveness of the information retrieval. The search engine is based on Elasticsearch. A multitude of information retrieval techniques are tested, with the goal of identifying the ones leading to a performance improvement. The techniques' effectiveness is measured using the evaluation measures: P@20, MAP, and BPref. The techniques explored that yield improvement in the search are: custom analyzers, filters, relevance feedback and reciprocal rank fusion. Other tested techniques, that yield negligible results, are: field boosting, bigrams and distance feature. Ultimately, the results are compared to the ones obtained by others in the Challenge.The TREC_COVID Challenge has the goal to create search engines to effectively and efficiently retrieve information produced at a rate never seen before, in the biomedical field. This work focuses on the effectiveness of the information retrieval. The search engine is based on Elasticsearch. A multitude of information retrieval techniques are tested, with the goal of identifying the ones leading to a performance improvement. The techniques' effectiveness is measured using the evaluation measures: P@20, MAP, and BPref. The techniques explored that yield improvement in the search are: custom analyzers, filters, relevance feedback and reciprocal rank fusion. Other tested techniques, that yield negligible results, are: field boosting, bigrams and distance feature. Ultimately, the results are compared to the ones obtained by others in the Challenge

RETRIEVAL, ACTION AND THE REPRESENTATION OF DISTANCE IN COGNITIVE MAPS

This thesis examines the context effects on retrieval, and the influence of action on the representation of distance in cognitive maps. It is proposed that bias in distance estimation is a function of the contexts of retrieval that trigger the representation of action in memory during evaluation tasks. The proposal is consistent with embodied cognition evidence that suggests that actions are implicitly a part of the representation, and will be naturally extracted as part of the retrieval process. The experimental work presented examines two different contextual cues; the frequency of visitation to landmarks, and the importance of activity performed at landmarks. Each cue primes differently the conceptualisation of landmarks prior to making distance estimation. This priming facilitates memory access, which fleshes out relevant spatial information from cognitive maps that are used in distance estimation and route description. This proposal was examined in a series of four experiments that employed structured interviews. Participants had to rate landmarks based on frequency of visitation criteria or importance of activity criteria, or both. They then made verbal distance estimations and route descriptions. The results found implicate the involvement of action representation. The involvement of action in cognitive process was empirically investigated in three further experiments. A new methodology was developed featuring the use of a blindfold, linguistic descriptions, and control of actual movements. Blindfolded participants learned new environments through verbal descriptions by imagining themselves walking in time with the metronome beats. During turns, they were carefully moved. Following instructions, they performed an action at mid-route. Their memories for the newly learned environments were tested through recalls and measured again with the metronome beats. The results found were consistent with explanations based on network-map theory. They implicate attentional processes as an intrinsic part of the cognitive mechanism, and the strings of the network-map as the actual motor program that executes the movement. These results are discussed in relation to the nature of cognitive maps