Color space and color channel selection on image segmentation of food images

Image segmentation is a predefined process of image processing to determine a specific object. One of the problems in food recognition and food estimation is the lack of quality of the result of image segmentation. This paper presents a comparative study of different color space and color channel selection in image segmentation of food images. Based on previous research regarding image segmentation used in food leftover estimation, this paper proposed a different approach to selecting color space and color channel based on the score of Intersection Over Union (IOU) and Dice from the whole dataset. The color transformation is required, and five color spaces were used: CIELAB, HSV, YUV, YCbCr, and HLS. The result shows that A in LAB and H in HLS are better to produce segmentation than other color channels, with the Dice score of both is 5 (the highest score). It concludes that this color channel selection is applicable to be embedded in the Automatic Food Leftover Estimation (AFLE) algorithm

Facial Expression Recognition using Residual Convnet with Image Augmentations

During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID 19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model. The code is available at https://github.com/yusufrahadika-facial-expressions-essay

Image Processing for Rapidly Eye Detection based on Robust Haar Sliding Window

Object Detection using Haar Cascade Clasifier widely applied in several devices and applications as a medium of interaction between human and computer such as a tool control that utilizes the detection of eye movements. Obviously speed and precision in the detection process such as eyes, has an effect if implemented on a device. If the eye could not detect accurately, controlling device systems could reach bad detection as well. The proposed method can be used as an approach to detect the eye region of eye based on haar classifier method by means of modifying the direction of sliding window. In which, it was initially placed in the middle position of image on facial area by assuming the location of eyes area in the central region of the image. While the window region of conventional haar cascade scan the whole of image start from the left top corner. From the experiment by using our proposed method, it can speed up the the computation time and improve accuracy significantly reach to 92,4%

Movie recommender systems using hybrid model based on graphs with co-rated, genre, and closed caption features

A movie recommendation is a long-standing challenge. Figuring out the viewer’s interest in movies is still a problem since a huge number of movies are released in no time. In the meantime, people cannot enjoy all available new releases or unseen movies due to their limited time. They also still need to choose which movies to watch when they have spare time. This situation is not good for the movie business too. In order to satisfy people in choosing what movies to watch and to boost movie sales, a system that can recommend suitable movies is required, either unseen in the past or new releases. This paper focuses on the hybrid approach, a combination of content-based and collaborative filtering, using a graph-based model. This hybrid approach is proposed to overcome the drawbacks of combination in the content-based and collaborative filtering. The graph database, Neo4j is used to store the collaborative features, such as movies with its genres, and ratings. Since the movie’s closed caption is rarely considered to be used in a recommendation, the proposed method evaluates the impact of using this syntactic feature. From the early test, the combination of collaborative filtering and content-based using closed caption gives a slightly better result than without closed caption, especially in finding similar movies such as sequel or prequel

Seleksi Fitur Menggunakan Ekstraksi Fitur Bentuk, Warna, Dan Tekstur Dalam Sistem Temu Kembali Citra Daun

Fitur yang digunakan untuk mengenali jenis daun meliputi bentuk, warna, dan tekstur. Tidak semua jenis fitur perlu digunakan untuk melakukan komputasi hasil ektra ksi, namun perlu diseleksi beberapa fitur yang paling berpengarauh dalam sistem temu kembali citra daun. Teknik seleksi fitur Correlation based Featured Selection (CFS) digunakan untuk melakukan pemilihan fitur berdasarkan korelasi antar fitur, sehingga dapat meningkatkan performa dari sistem temu kembali citra daun. Jenis seleksi fitur yang digunakan diantaranya menggunaka CFS, CFS dengan Genetic Search (GS), dan chi square. Analisis keterkaitan korelasi antar fitur melalui seleksi fitur juga dikombinasikan dengan penggunaan kedekatan dalam menghitung similaritas pada sistem temu kembali. Penggunaan kedekatan dengan Lp norm, ma nhattan, euclidean, cosine, dan mahalanobis. Hasil penelitian ini menunjukkan nilai temu kembali paling tinggi ketika menggunakan seleksi fitur CFS dengan pengukuran kedekatan mahalanobis

Hybrid Head Tracking for Wheelchair Control Using Haar Cascade Classifier and KCF Tracker

Disability may limit someone to move freely, especially when the severity of the disability is high. In order to help disabled people control their wheelchair, head movement-based control is preferred due to its reliability. This paper proposed a head direction detector framework which can be applied to wheelchair control. First, face and nose were detected from a video frame using Haar cascade classfier. Then, the detected bounding boxes were used to initialize Kernelized Correlation Filters tracker. Direction of a head was determined by relative position of the nose to the face, extracted from tracker’s bounding boxes. Results show that the method effectively detect head direction indicated by 82% accuracy and very low detection or tracking failure

Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor  (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection

Pembentukan Daftar Stopword Menggunakan Term Based Random Sampling Pada Analisis Sentimen Dengan Metode Naïve Bayes (Studi Kasus: Kuliah Daring Di Masa Pandemi)

Stopword Removal merupakan bagian dari tahapan preprocessing teks yang bertujuan untuk menghapus kata yang tidak relevan didalam suatu kalimat berdasarkan daftar stopword. Daftar stopword yang biasa digunakan berbentuk digital library yang daftarnya sudah tersedia sebelumnya, namun tidak semua kata-kata yang terdapat didalam digital library merupakan kata yang tidak relevan dalam suatu data tertentu. Penelitian ini menggunakan daftar stopword yang dibentuk dengan algoritme Term Based Random Sampling. Dalam Term Based Random Sampling terdapat 3 parameter yaitu Y untuk jumlah perulangan pengambilan kata random, X untuk jumlah pengambilan bobot terendah dalam perulangan Y, dan L sebagai persentase jumlah stopword yang ingin digunakan. Sehingga penelitian ini ditujukan untuk mencari kombinasi terbaik dari 3 parameter tersebut serta membandingkan stopword Term Based Random Sampling dengan stopword Tala dan tanpa proses stopword removal dalam analisis sentimen tweet mengenai kuliah daring dengan menggunakan metode Naïve Bayes. Hasil evaluasi dengan stopword Term Based Random Sampling mendapatkan akurasi tertinggi dengan X, Y, L sebesar 10, 10, 40 dengan macroaverage accuracy sebesar 0,758, macroaverage precision sebesar 0,658, macroaverage recall sebesar 0,636, dan macroaverage f-measure sebesar 0,647. Berdasarkan hasil pengujian disimpulkan bahwa semakin besar X, Y, L maka semakin tinggi kemungkinannya untuk hasil evaluasi turun. Hasil pengujian membuktikan bahwa Term Based Random Sampling berhasil mendapatkan akurasi lebih tinggi dibandingkan dengan stopword Tala maupun tanpa menggunakan proses stopword removal. AbstractStopword Removal is part of the text preprocessing stage which aims to remove irrelevant words in a sentence based on the stopword list. The stopword list that is commonly used is in the form of a digital library whose list is already available, but not all words contained in the digital library are irrelevant words in certain data. This study uses a stopword list formed by the Term Based Random Sampling algorithm. In Term Based Random Sampling, there are 3 parameters, namely Y for the number of random word retrieval repetitions, X for the lowest number of weights in Y repetitions, and L as the percentage of the number of stopwords you want to use. So this research is aimed at finding the best combination of these 3 parameters and comparing the Term Based Random Sampling stopword with the stopword tuning and without the stopword removal process in the analysis of tweet sentiment regarding online lectures using the Naïve Bayes method. The results of the evaluation with the Term Based Random Sampling stopword get the highest accuracy with X, Y, L of 10, 10, 40 with a macroaverage accuracy of 0.758, a macroaverage precision of 0.658, a macroaverage recall of 0.636, and a macroaverage f-measure of 0.647. Based on the test results, it is concluded that the greater the X, Y, L, the higher the probability that the evaluation results will decrease. The test results prove that Term Based Random Sampling is successful in obtaining higher accuracy than stopword tuning or without using the stopword removal process

User Emotion Identification in Twitter Using Specific Features: Hashtag, Emoji, Emoticon, and Adjective Term

Twitter is a social media application, which can give a sign for identifying user emotion. Identification of user emotion can be utilized in commercial domain, health, politic, and security problems. The problem of emotion identification in twit is the unstructured short text messages which lead the difficulty to figure out main features. In this paper, we propose a new framework for identifying the tendency of user emotions using specific features, i.e. hashtag, emoji, emoticon, and adjective term. Preprocessing is applied in the first phase, and then user emotions are identified by means of classification method using kNN. The proposed method can achieve good results, near ground truth, with accuracy of 92%