Food Category Recognition Using SURF and MSER Local Feature Representation

Abstract

Food object recognition has gained popularity in recent years. This can perhaps be attributed to its potential applications in fields such as nutrition and fitness. Recognizing food images however is a challenging task since various foods come in many shapes and sizes. Besides having unexpected deformities and texture, food images are also captured in differing lighting conditions and camera viewpoints. From a computer vision perspective, using global image features to train a supervised classifier might be unsuitable due to the complex nature of the food images. Local features on the other hand seem the better alternative since they are able to capture minute intricacies such as interest points and other intricate information. In this paper, two local features namely SURF (Speeded-Up Robust Feature) and MSER (Maximally Stable Extremal Regions) are investigated for food object recognition. Both features are computationally inexpensive and have shown to be effective local descriptors for complex images. Specifically, each feature is firstly evaluated separately. This is followed by feature fusion to observe whether a combined representation could better represent food images. Experimental evaluations using a Support Vector Machine classifier shows that feature fusion generates better recognition accuracy at 86.6%.

Switch Effect, Water‐Like State, and New Interaction Mechanism of an H‐Bonded Polycation–Polyacrylamide System to Realize Instant Decolorization of Dyeing Wastewater

“…According to the current understanding, charge neutralization and bridging are regarded as the most basic two interaction mechanisms of flocculants. [22][23][24] For the flocculation…”

“…The discovery of new cohesive structure-forms of serial flocs disrupts the present understanding, which claims that the bridging structures are the final structure-forms of flocs. [22][23][24] Because of the abovementioned newly discovered essential characteristics of the WLPS, the WLPS realized an instantaneous and highly adaptable decolorization of dyeing wastewater. Moreover, the trial application of the WLPS in a real engineering line for treating dyeing wastewater was successfully carried out, confirming that the WLPS can be directly popularized for the practical engineering of water treatment at present.…”

“…[13,[19][20][21] According to the current understanding, the flocculation decolorization of dyeing wastewater primarily depends on the two basic processes of charge neutralization and bridging. [22][23][24] Small precipitations are first formed by the electrostatic interactions (i.e., charge neutralization) between the polycationic flocculants and the anionic dye (it takes a relatively long time as a rate-controlling step). Subsequently, the small precipitations can be gradually aggregated into the larger flocs by the bridging interactions (as the final structure assumes flocs).…”

Improving Invisible Food Texture Detection by using Adaptive Extremal Region Detector in Food Recognition

“…Then, all the new set of the quantity of ER will be updated in cell array before the features are extracted by using Speeded Up Robust Feature (SURF) descriptor. SURF is chosen to be paired with MSER due its balanced performance between accuracy and efficiency, less sensitive to noise and more practical for real time application [10], [11], [14]. Furthermore, SURF generates shorter length of feature vector that was reasoned for a speedy feature encoding process, produced a distinctive feature and robust to the geometric and photometric deformation.…”

“…There are numerous types of local features in the literature. A research conducted by [10] employed the interest region detector by using Maximally Stable Extremal Region (MSER) to detect food interest points, considering MSER as among the best interest region detector in term of effectiveness and efficiency [11]. MSER detects a set of connected regions from an image to define the extremal regions (ER).…”